Cell free dna deconvolusion and use thereof

ABSTRACT

Methods of determining the origin of cell free DNA (cfDNA) and for detecting death of a cell type or tissue in a subject by determining the origin of cfDNA in the subject are provided. Computer program products, computerized methods and computerized systems for doing same, as well as methods for constructing a methylome atlas, are also provided.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority of U.S. Provisional Patent Application No. 62/631,791, filed Feb. 18, 2018, and U.S. Provisional Patent Application No. 62/661,179, filed Apr. 23, 2018, the contents of which are all incorporated herein by reference in their entirety.

FIELD OF INVENTION

The present invention is in the field of cell free DNA methylome analysis.

BACKGROUND OF THE INVENTION

Small fragments of DNA circulate freely in the peripheral blood of healthy and diseased individuals. These cell-free DNA (cfDNA) molecules are thought to derive from dying cells and are thus a reflection of ongoing cell death taking place in the body. In recent years, this understanding has led to the emergence of diagnostic tools which are impacting multiple areas of medicine. Specifically, next generation sequencing of fetal DNA circulating in maternal blood has allowed non-invasive prenatal testing of fetal chromosomal abnormalities; detection of donor-derived DNA in the circulation of organ transplant recipients can be used for early identification of graft rejection; and the evaluation of mutated DNA in the circulation can be used to detect, genotype and monitor cancer. These technologies are powerful at identifying genetic anomalies in circulating DNA yet are not informative when cfDNA does not carry mutations. A key limitation is that sequencing does not reveal the tissue origins of cfDNA, precluding the identification of tissue-specific cell death. The latter is critical in many settings such as neurodegenerative, inflammatory or ischemic diseases, not involving DNA mutations. Even in oncology, it is often important to determine the tissue origins of the tumor in addition to determining its mutational profile, for example in cancers of unknown primary (CUP) and in the setting of early cancer diagnosis. Finally, identification of collateral tissue damage (e.g. toxicity of drugs in genetically normal tissues) is a key element in drug development and monitoring of response to therapy.

Several approaches have been proposed recently for tracing the tissue sources of cfDNA, all based on tissue-specific epigenetic signatures. Nucleosome positioning in different tissues has been used to infer the origins of cfDNA, based on the idea that nucleosome-free regions are more likely to be degraded upon cell death and hence will be under-represented in cfDNA. An alternative approach is based on DNA methylation patterns. Methylation of cytosine adjacent to guanine (CpG sites) is an essential component of cell type-specific gene regulation, and hence is a fundamental mark of cell identity. It was recently shown that the detection of cfDNA molecules with a methylation pattern unique to a certain tissue can be used to identify cell death derived from that tissue. Others have taken a genome-wide approach to the problem and used the plasma methylome to assess the origins of cfDNA. It has been showed that the deconvolution of cfDNA methylation profiles can be used to infer relative contributions of four different tissues, using a low-depth whole genome bisulfite sequencing (WGBS). It was further demonstrated that the potential exists of using cfDNA methylation in detecting cancer DNA in circulation, as well as identifying its tissue of origin in two cancer types, using a reduced representation bisulfite sequencing (RRBS) approach. Nevertheless, a method of reliably determining the tissue of origin of all cfDNA found in a subject, and not just the most abundant is greatly in need. Such a method would allow for early diagnosis of conditions that may be unknown to the physician or patient, and not just a directed assessment of an already predicted condition.

SUMMARY OF THE INVENTION

The present invention provides methods of determining the origin of cell free DNA (cfDNA) and for detecting death of a cell type or tissue in a subject by determining the origin of cfDNA in the subject are provided. Computer program products for doing same and methods of constructing a methylome atlas are also provided.

According to a first aspect, there is provided a method of determining the cell type or tissue of origin of cell free DNA (cfDNA) comprising:

-   -   a. providing cfDNA;     -   b. measuring DNA methylation of the cfDNA; and     -   c. assigning a cfDNA molecule from the cfDNA to a cell type or         tissue of origin by comparing the methylation of the molecule to         a methylome atlas of at least 1 cell type or tissue, wherein the         atlas comprises at least 25 of the 100 most uniquely methylated         sites and at least 25 of the 100 most uniquely unmethylated         sites in each of the at least 1 cell type or tissue;         thereby determining the cell or tissue of origin of cfDNA.

According to another aspect, there is provided a method of detecting death of a cell type or tissue in a subject comprising:

-   -   a. providing cfDNA from the subject;     -   b. measuring DNA methylation of the cfDNA; and     -   c. assigning a cfDNA molecule from the cfDNA to a cell type or         tissue of origin by comparing the methylation of the molecule to         a methylome atlas of at least 1 cell type or tissue, wherein the         atlas comprises at least 25 of the 100 most uniquely methylated         sites and at least 25 of the 100 most uniquely unmethylated         sites in each of the at least 1 cell type or tissue;         thereby detecting death of a cell type or tissue in a subject.

According to some embodiments, at least 50 ng of cfDNA are provided. According to some embodiments, the providing comprises providing a bodily fluid and isolating the cfDNA from the bodily fluid.

According to some embodiments, the measuring DNA methylation comprises bisulfite conversion of the cfDNA. According to some embodiments, the measuring further comprises performing a methylome array or chip on the bisulfite converted cfDNA.

According to some embodiments, the methylome atlas comprises only data from purified cell types. According to some embodiments, the atlas comprises only data from non-blood derived purified cell types. According to some embodiments, the methylome atlas comprises methylation data from at least 5 of the following 34 tissues or cell types: monocytes, B-cells, CD4+ T-cells, NK-cells, CD8+ T-cells, eosinophils, neutrophils, erythrocyte progenitors, adipocytes, neurons, hepatocytes, lung alveolar cells, pancreatic beta cells, pancreatic acinar cells, pancreatic duct cells, vascular endothelial cells, left atrium, bladder, breast, cervix, colon, esophagus, oral cavity, kidney, prostate, rectum, stomach, thyroid, uterus, lung bronchial cells, cholangiocytes, muscle, oligodendrocytes, and ovary. According to some embodiments, the methylome atlas comprises data from all of the 34 tissues or cell types.

According to some embodiments, the methylome atlas comprises at least the 100 most uniquely methylated or unmethylated sites in each tissue or cell type. According to some embodiments, the methylome atlas further comprises any CpG sites within at least 150 base pairs upstream and downstream of the most uniquely methylated and most uniquely unmethylated sites in each tissue or cell type. According to some embodiments, the CpG sites within at least 150 base pairs upstream and downstream are selected from Tables 1 and 2. According to some embodiments, the methylome atlas further comprises at least one of the 500 CpG sites that best differentiate between the most similar pairs of tissues and cell types. According to some embodiments, the 500 CpG sites that best differentiate between the most similar pairs of tissues and cell types are selected from Table 3. According to some embodiments, the most uniquely methylated sites are selected from Table 1. According to some embodiments, the most uniquely hypomethylated sites are selected from Table 2.

According to some embodiments, cfDNA of the tissue or cell type comprises as little 1% of all of the cfDNA.

According to some embodiments, the methods of the invention are for use in detecting a disease state in a subject in need thereof and wherein the cfDNA is from the subject. According to some embodiments, the disease state is selected from organ transplantation, sepsis, and cancer. According to some embodiments, the disease is cancer, and the method determines the cell or tissue of origin of the cancer.

According to another aspect, there is provided a computer program product for determining the cell or tissue of origin of cell free DNA (cfDNA), comprising a non-transitory computer-readable storage medium having program code embodied thereon, the program code executable by at least one hardware processor to

-   -   a. measure or access measurements of DNA methylation of cfDNA;     -   b. assign a cfDNA molecule from the cfDNA to a cell type or         tissue of origin by comparing the methylation of the molecule to         a methylome atlas of at least 5 cell types or tissues, wherein         the atlas comprises at least 25 of the 100 most uniquely         methylated sites and at least 25 of the 100 most uniquely         unmethylated sites in each of the 5 cell types or tissues; and     -   c. provide an output regarding the cell or tissue of origin of         cfDNA.

According to another aspect, there is provided a method of constructing a methylome atlas, comprising:

-   -   a. providing DNA methylation data from at least 5 tissues and/or         cell types;     -   b. selecting at least the top 100 uniquely hyper-methylated and         at least the top 100 uniquely hypomethylated CpG sites per a         tissue and/or cell type as compared to the other tissues and/or         cell types; and     -   c. combining the uniquely hyper-methylated and uniquely         hypo-methylated sites for all the tissues and/or cell types;         thereby constructing a methylome atlas.

According to some embodiments, the methods of the invention further comprise:

-   -   d. selecting all neighboring CpG sites within at least 150 base         pairs upstream and/or downstream of each of the uniquely         hyper-methylated and uniquely hypomethylated CpG site; and     -   e. combining the neighboring CpG sites with the uniquely         hyper-methylated and uniquely hypomethylated CpG sites.

According to some embodiments, the methods of the invention further comprise:

-   -   f. selecting at least the top 100 CpG sites that best         differentiate between the most similar pair of tissues and/or         cell types; and     -   g. combining the CpG sites that best differentiate with the         uniquely hyper-methylated and uniquely hypomethylated CpG sites.

According to another aspect, there is provided a computerized method of determining the cell type or tissue of origin of cell free DNA (cfDNA) comprising:

using at least one hardware processor to:

-   -   a. receive a methylation sequencing data acquired from a cfDNA         sample and a methylome atlas, wherein the methylome atlas         comprises (i) a first set of uniquely methylated sites, (ii) a         plurality of neighboring methylated sites, wherein at least some         of the plurality of neighboring methylated sites are within 500         base units to each element of the first set of uniquely         methylated sites, and (iii) a second set of uniquely methylated         sites comprising more than double the number of sites of the         first set, and comprising less than half the number of tissue         types in each comparison as the first set;     -   b. compare the methylation sequencing data with a methylome         atlas to determine a set of candidate tissues, wherein the         comparing comprises identifying at least some elements of the         first set of uniquely methylated sites in both the methylation         sequencing data and the methylome atlas;     -   c. compare the plurality of neighboring methylated sites of the         methylation sequencing data with the atlas entries of the set of         candidate tissues to determine a first subset of candidate         tissues;     -   d. compare the second set of uniquely methylated sites of the         methylome atlas within the methylation sequencing data to         determine a probability ranking of candidate tissues;     -   e. assign at least one of the highest-ranking tissues of the         probability ranking of candidate tissues to the cfDNA sample.

According to another aspect, there is provided a computer program product for determining the cell or tissue of origin of cell free DNA (cfDNA), comprising a non-transitory computer-readable storage medium having program code embodied thereon, the program code executable by at least one hardware processor to:

-   -   a. receive a methylation sequencing data acquired from a cfDNA         sample and a methylome atlas, wherein the methylome atlas         comprises (i) a first set of uniquely methylated sites, (ii) a         plurality of neighboring methylated sites, wherein at least some         of the plurality of neighboring methylated sites are within 500         base units to each element of the first set of uniquely         methylated sites, and (iii) a second set of uniquely methylated         sites comprising more than double the number of sites of the         first set, and comprising less than half the number of tissue         types in each comparison as the first set;     -   b. compare the methylation sequencing data with a methylome         atlas to determine a set of candidate tissues, wherein the         comparing comprises identifying at least some elements of the         first set of uniquely methylated sites in both the methylation         sequencing data and the methylome atlas;     -   c. compare the plurality of neighboring methylated sites of the         methylation sequencing data with the atlas entries of the set of         candidate tissues to determine a first subset of candidate         tissues;     -   d. compare the second set of uniquely methylated sites of the         methylome atlas within the methylation sequencing data to         determine a probability ranking of candidate tissues;     -   e. assign at least one of the highest-ranking tissues of the         probability ranking of candidate tissues to the cfDNA sample.

According to another aspect, there is provided a computerized system for determining the cell or tissue of origin of cell free DNA (cfDNA), comprising: at least one hardware processor; and a non-transitory computer-readable storage medium having program code embodied thereon. The program code executable by the at least one hardware processor to:

-   -   a. receive a methylation sequencing data acquired from a cfDNA         sample and a methylome atlas, wherein the methylome atlas         comprises (i) a first set of uniquely methylated sites, (ii) a         plurality of neighboring methylated sites, and (iii) a second         set of uniquely methylated sites comprising more than double the         number of sites of the first set. The second set of uniquely         methylated sites comprise at less than half the number of tissue         types in each comparison as the first set of the first set. At         least some of the plurality of neighboring methylated sites are         within 500 base units to each element of the first set of         uniquely methylated sites,     -   b. compare the methylation sequencing data with a methylome         atlas to determine a set of candidate tissues, wherein the         comparing comprises identifying at least some elements of the         first set of uniquely methylated sites in both the methylation         sequencing data and the methylome atlas.     -   c. compare the plurality of neighboring methylated sites of the         methylation sequencing data with the atlas entries of the set of         candidate tissues to determine a first subset of candidate         tissues;     -   d. compare the second set of uniquely methylated sites of the         methylome atlas within the methylation sequencing data to         determine a probability ranking of candidate tissues;     -   e. assign at least one of the highest-ranking tissues of the         probability ranking of candidate tissues to the cfDNA sample.

According to some embodiments, the first set comprises between 25 and 100 most uniquely methylated sites.

According to some embodiments, the first set comprises a plurality of most uniquely methylated sites and wherein the plurality of neighboring methylated sites comprises any CpG sites within between 150 and 500 base pairs upstream and downstream of said most uniquely methylated sites in each tissue or cell type.

According to some embodiments, the second set comprises between 100 and 500 most uniquely methylated sites.

According to some embodiments, the second set of uniquely methylated sites compares a plurality of specific pairs or triplets of tissue types, such as similar tissue types, or the like.

According to some embodiments, the first set of uniquely methylated sites are uniquely methylated as compared to all cell types and tissues of the atlas, and wherein the second set of uniquely methylated sites are uniquely methylated in one cell type or tissue as compared to a second most similar cell type or tissue.

According to some embodiments, the comparing comprises using a latent probabilistic model.

Further embodiments and the full scope of applicability of the present invention will become apparent from the detailed description given hereinafter. However, it should be understood that the detailed description and specific examples, while indicating preferred embodiments of the invention, are given by way of illustration only, since various changes and modifications within the spirit and scope of the invention will become apparent to those skilled in the art from this detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments of the invention are herein described, by way of example only, with reference to the accompanying drawings. With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of embodiments of the invention. In this regard, the description together with the drawings makes apparent to those skilled in the art how embodiments of the invention may be practiced.

FIGS. 1A-E: Feature selection results using 30 Illumina Infinium 450K/EPIC arrays. (1A) Heat map of 30 CpGs (rows) selected using Compressed Sensing algorithm. (1B) Heat map of 60 CpGs selected using two consecutive rounds of Compressed Sensing. (1C) Heat map of 1000 CpGs selected by first using Compressed Sensing (same as 1A, 1B), followed by 940 iterations of feature selection using error-correcting code. (1D) A line graph, with shaded area showing the improvement in the minimal distance between two tissues, for 1000 iterations of the algorithm. The red line shows the minimal tissue distance for the variance-based algorithm. The horizontal yellow line shows the minimal tissue distance obtained by selecting the 30 most specific CpGs per tissue (900 CpGs overall). (1E) A bar graph showing repeating the feature selection algorithms 100 times (each time removing previously selected CpGs), which demonstrates the efficiency of the algorithm as well as the limited number of informative CpG sites.

FIGS. 2A-C: Comprehensive reference methylation matrix. (2A) A heatmap showing the 100 most uniquely hyper-methylated and 100 hypomethylated sites for each cell type. Selection of CpGs is described in the Materials and Methods section. (2B) Bar chart estimations of specificity (false positive rate) and sensitivity (detection rate) calculated for various CpG selection strategies. (2C) A cartoon schematic of a method of the invention.

FIGS. 3A-E: Deconvolution of simulated mixed samples. (3A-B) Line graphs showing the actual and predicted contribution of methylomes of indicated cell types, whole tissues and cell cultures after mixing in silico with the whole blood methylome, and deconvolution of the resulting mix (3A) with and (3B) without feature selection. Solid black lines represent the median predicted percent contributed by each cell type for each actual contribution. The blue shaded area represents the 25%-75% confidence interval. (3C-D) Line graph comparing the performance of a reference matrix containing either purified cell types, cultured cells or whole tissue methylomes. For each cell type indicated, the solid green line represents the predicted percent contributed by this cell type at the relevant actual contribution, using the comprehensive reference matrix. (3C) The blue lines represent the predicted contribution of these cells, when pancreatic cells in the reference matrix are replaced by whole pancreas methylome. (3D) The blue/red solid lines represent the predicted contribution of these cells, when hepatocytes in the reference matrix are replaced with whole liver or HepG2 culture cells methylome respectively. (3E) A line graph as in 3C but using a reference atlas composed of an average healthy cfDNA methylome and three pancreas cell types (acinar, beta, and duct cells).

FIGS. 4A-C: Cellular contributors to cfDNA in healthy individuals. (4A) A pie chart of the average predicted distributions of contributors to cfDNA, across 8 pooled samples. (4B) A bar chart showing results of deconvolution of 8 pooled DNA samples in absolute levels of DNA (genome equivalents/ml, derived by multiplying the fraction contribution of each tissue by the total concentration of cfDNA). Cell types which contributed less than 1.5% were included in “Other”. Young <30 years old, old >75 years old. (4C) Spread plots showing comparisons of proportions predicted by deconvolution of cfDNA or leukocyte methylome for erythrocyte progenitors, vascular endothelial cells and hepatocytes. In all cases, the contribution predicted for cfDNA was higher than for leukocytes (p<0.05). Also shown, for control, are spread plots from lymphocytes and granulocytes (which are the prevalent cells that make up leukocytes, as expected).

FIGS. 5A-E: Cellular contributors to cfDNA in pancreatic islet transplantation. (5A) Pie chart showing result of deconvolution of pooled sample of cfDNA from 5 patients 1 hour after islet transplantation. Cell types which contributed less than 1% were included in “Other”. (5B) A bar graphs showing the result of deconvolution of pooled post-transplant sample and an average of healthy pools, shown in absolute levels of DNA (genome equivalents/ml). (5C) A line graph of the amount of pancreatic cfDNA predicted for three individuals before, 1 hour after and 2 hours after transplantation. (5D) A line graph showing a comparison of pancreatic cfDNA measurements using methylome deconvolution vs targeted methylation assay. Results are highly similar (r=0.988, p-value=8.5e-08). (5E) A line graph showing the benefit of using purified cell types in reference. Amount of pancreatic cfDNA predicted is shown for three individuals before, 1 hour after and 2 hours after transplantation, using a reference matrix including methylomes from either purified pancreatic cells (solid lines, also displayed in 5C) or whole pancreas (dotted lines).

FIGS. 6A-E: Cellular contributors to cfDNA in sepsis. (6A) A bar chart of predicted cellular contributions for 15 samples of cfDNA from patients with sepsis. Cell types which contributed less than 1% were included in “Other”. Average of healthy pools is shown at the right. (6B-D) Pie charts representing predicted distribution of cell types contributing to cfDNA from 3 of the sepsis samples shown, including a subject with only sepsis (6B), a subject whose sepsis developed after extraction of a colon cancer that had metastasized to the liver (6C) and a subject with liver damage in addition to sepsis (6D). (6E) A line graph showing a comparison of predicted levels of hepatocyte cfDNA to serum levels of Alanine Aminotransferase, a standard biomarker for hepatocyte damage (r=0.926, p-value=7.1e-07.

FIG. 7A-B: Cellular contributors to cfDNA in cancer. (7A) A bar chart showing the predicted contributions of cell types for 3 samples of cfDNA from patients with colon cancer. Sample CC3 is the same as sample 51 from FIG. 6A. Average of healthy pools is shown as well. (7B) A heat map of the predicted cellular contributors for 3 cfDNA samples obtained from patients diagnosed with a Cancer of Unknown Primary (CUP). Cell types which contributed less than 1% were considered as 0. The contribution of blood cell types is not shown. For each patient, the location of metastases and the predicted tissue source of cancer according to clinical history are listed.

FIGS. 8A-B: Reproducibility of deconvolution results. (8A) Bar charts of the predicted distribution of cellular contributors from 4 samples using different amounts of cfDNA (50 ng, 100 ng or 250 ng). (8B) Pearson correlation coefficients shown for different samples analyzed from the same individual with varying amounts of cfDNA used.

DETAILED DESCRIPTION OF THE INVENTION

The present invention provides methods of determining the origin of cell free DNA (cfDNA) and detecting death of a cell type or tissue in a subject by determining the origin of cfDNA in the subject. The methods of the invention are based on the surprising finding that by generating an atlas of informative methylation sites for various tissues and cell types cfDNA methylation sequencing can be deconvoluted to accurately identify the origin of cfDNA molecules even when they are a very small percentage of the total DNA sampled. The methods of the invention are further based on the surprising findings that use of purified cell types in place of whole tissues and an atlas comprising the most uniquely methylated and unmethylated sites between the different tissues/cell types provided superior deconvolution results.

By a first aspect, there is provided a method of determining the cell or tissue of origin of a cell free DNA (cfDNA) comprising:

-   -   a. providing cfDNA;     -   b. measuring DNA methylation of the cfDNA; and     -   c. assigning a cfDNA molecules from the cfDNA to a cell type or         tissue of origin by comparing the methylation of the molecule to         a methylome atlas of at least 1 cell type or tissue, wherein the         atlas comprises at least 25 of the 100 most uniquely methylated         sites and at least 25 of the 100 most uniquely unmethylated         sites in each of the at least 1 cell type or tissue;         thereby determining the cell or tissue of origin of cfDNA.

In some embodiments, the cfDNA is from a subject and assigning a cfDNA molecule to a cell or tissue of origin indicates detection of death of that cell or tissue. In some embodiments, the subject is suspected of having increased cell death. In some embodiments, the subject is not suspected of having increased cell death. In some embodiments, the subject appears healthy and/or does not suffer from a disease or condition.

By another aspect, there is provided a method of detecting death of a cell type or tissue in a subject comprising:

-   -   a. providing cfDNA from the subject;     -   b. measuring DNA methylation of the cfDNA; and     -   c. assigning a cfDNA molecules from the cfDNA to a cell type or         tissue of origin by comparing the methylation of the molecule to         a methylome atlas of at least 1 cell type or tissue, wherein the         atlas comprises at least 25 of the 100 most uniquely methylated         sites and at least 25 of the 100 most uniquely unmethylated         sites in each of the at least 1 cell type or tissue;         thereby detecting death of a cell type or tissue in a subject.

As used herein, “cfDNA” refers to any DNA obtained from an organism which existed in the organism outside of a cell. In some embodiments, the cfDNA is DNA obtained from an organism which existed in the organism outside of any vesicle. Cell-free DNA is well known in the art, and generally refers to DNA that is free floating within a bodily fluid. This DNA is generally not enclosed in a vesicle and thus DNA in transport, such as by exosomes or other vesicular transporters, in not considered cfDNA. In some embodiments, cfDNA is DNA from a dying and/or dead cell. When a cell dies the DNA is generally fragmented and released from the cell as it lyses. This DNA however, is not all immediately removed or cleaned up and thus persists in the organism. Frequently the DNA from the dead cell enters the bloodstream.

Since cfDNA has a short half-life in the organism, it provides a snap shot of the cell death occurring in the organism at that moment. In some embodiments, the methods of the invention detect cell death that has occurred within the last 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, or 60 minutes from the time of providing the cfDNA. Each possibility represents a separate embodiment of the invention.

In some embodiments, the cfDNA is mammalian cfDNA. In some embodiments, the cfDNA is human cfDNA. In some embodiments, the cfDNA is extracted from bodily fluid. In some embodiments, the providing comprises providing a bodily fluid and isolating the cfDNA from the bodily fluid. In some embodiments, the bodily fluid is blood. In some embodiments, the bodily fluid is selected from at least one of: blood, serum, gastric fluid, intestinal fluid, saliva, bile, tumor fluid, interstitial fluid, and stool. Standard techniques for cell-free DNA extraction are known to a skilled artisan, a non-limiting example of which is the QIAamp Circulating Nucleic Acid kit (QIAGEN).

In some embodiments, at least 10, 20, 30, 40, 50, 60, 70, 80, 90 or 100 ng of cfDNA are provided. Each possibility represents a separate embodiment of the invention. In some embodiments, as little as 10, 20, 30, 40, 50, 60, 70, 80, 90 or 100 ng of cfDNA are provided. Each possibility represents a separate embodiment of the invention. In some embodiments, at least 50 ng are provided. In some embodiments, as little as 50 ng are provided.

In some embodiments, the providing comprises providing a bodily fluid and isolating the cfDNA from the bodily fluid. In some embodiments, the bodily fluid is selected from: blood, serum, gastric fluid, intestinal fluid, saliva, bile, tumor fluid, interstitial fluid, breast milk and stool. In some embodiments, the bodily fluid is any bodily fluid that contains cfDNA. In some embodiments, the bodily fluid is blood. In some embodiments, the bodily fluid is any one of whole blood, partially lysed whole blood, plasma, or partially processed whole blood.

The sample of blood can be obtained by standard techniques, such as using a needle and syringe. In another embodiment, the blood sample is a peripheral blood sample. Alternatively, the blood sample can be a fractionated portion of peripheral blood, such as a plasma sample. In another embodiment, once the blood sample is obtained, total DNA can be extracted from the sample using standard techniques known to one skilled in the art. In some embodiments, intact cells are removed before DNA extraction, so that only free-floating DNA is extracted. Intact cells can be removed by any method known in the art, such as for non-limiting example by centrifugation or by gradient separation, such as by Ficol gradient separation. A non-limiting example for DNA extraction is the FlexiGene DNA kit (QIAGEN). In another embodiment, maternal plasma may be further separated from peripheral blood by centrifugation, such as exemplified herein, at 1,900×g for 10 minutes at 4° C. The plasma supernatant may be re-centrifuged at 16,000×g for 10 minutes at 4° C. In another embodiment, a fraction of the resulting supernatant is used for cell-free DNA extraction, to thereby receive plasma DNA extracts. Standard techniques for receiving cell-free DNA extraction are known to a skilled artisan, a non-limiting example of which is the QIAamp Circulating Nucleic Acid kit (QIAGEN). In some embodiments, the total cfDNA is subsequently fragmented, such as to sizes of approximately 300 bp-800 bp. For example, the total DNA can be fragmented by sonication.

Measuring DNA methylation may be performed by any method known in the art. Non-limiting examples include deep sequencing following bisulfite conversion, ELISA-based methylation kits, methylation sensitive PCR, and the luminometric methylation assay (LUMA). In some embodiments, measuring DNA methylation comprises bisulfite conversion. In some embodiments, measuring DNA methylation further comprises next generation sequencing. In some embodiments, measuring DNA methylation further comprises next generation sequencing. In some embodiments, only the loci present in the atlas are sequenced. Next generation sequencing, also known as high-throughput sequencing is any sequencing method that allows for rapid high-throughput sequencing of base pairs from DNA or RNA samples. Such sequencing is well known in the art and can include Illumina arrays and ion torrent as non-limiting examples. Next generation sequencing of DNA methylation works on a similar principle and may be performed with arrays such as the Illumina EPIC array and the Illumina 450 k array, for example. In some embodiments, data from the whole genome is used. In some embodiments, data from chips or arrays are used. Such chip/array data may decrease background, lower costs, and provide more reliable cleaner data.

In some embodiments, the methylome atlas comprises data from at least 1, 3, 5, 10, 15, 20, or 25 tissues. Each possibility represents a separate embodiment of the invention. In some embodiments, the methylome atlas comprises data from at least 1, 3, 5, 10, 15, 20, or 25 cell types. Each possibility represents a separate embodiment of the invention. In some embodiments, the methylome atlas comprises data from at least 1, 3, 5, 10, 15, 20, or 25 tissues and/or cell types. Each possibility represents a separate embodiment of the invention. It will be understood by one skilled in the art that only tissues and/or cell types can be identified as the origin of the cfDNA is they are included in the atlas. Thus, for example, if only 10 tissues are selected than cfDNA can only be identified as coming from one of those tissues. Similarly, if it is predicted that the cfDNA may be from a particular source it may be less time consuming and cheaper to use a smaller atlas. For example, if hepatotoxicity is being examined, the atlas may comprise only the hepatocyte methylome, or only methylomes from liver cell types. In such a case the readout would be X % of the cfDNA is from dead hepatocytes/liver cells and the rest is unknown. If the possible source of cfDNA is unknown, or if a subject is healthy (or appears healthy) than a broader atlas comprising CpGs from more tissues would be preferred. In some embodiments, the atlas comprises data from at least 5 cell types and/or tissues.

In some embodiments, the atlas comprises data only from tissues. In some embodiments, the atlas comprises data only from cell types. In some embodiments, the atlas comprises data from tissues and cell types. In some embodiments, the cell types are purified cell populations. In some embodiments, the cell types comprise blood-derived purified cell populations. In some embodiments, the cell types comprise tissue-derived purified cell populations. In some embodiments, the atlas does not comprise data from blood-derived purified cell population. In some embodiments, the atlas consists of only tissue-derived purified cell population data. In some embodiments, atlas comprises data from blood-derived and tissue-derived purified cell populations. As used here, “blood-derived” and “tissue-derived” cell types or cell populations refer to a cell type or population whose source is either blood or a tissue or organ. Blood-derived population are well known, and may be red blood cells, monocytes, b-cells, t-cells, or the like. They may express specific markers, such as CD4-positive or CD-8 positive T cells, for non-limiting example. Tissue-derived cells are from a tissue or organ and not blood. All organs are made up for multitudes of cells that may be identified by markers, such as protein expression, surface expression, secretion or morphology. Examples include different neurons in the brain, and beta/duct/acinar cells in the pancreas.

In some embodiments, the methylome atlas comprises methylation data from at least 1, 2, 3, 4, 5, 10, 15, 20, 25, 30 or 34 of the following 34 tissues or cell types: monocytes, B-cells, CD4+ T-cells, NK-cells, CD8+ T-cells, eosinophils, neutrophils, erythrocyte progenitors, adipocytes, neurons, hepatocytes, lung alveolar cells, pancreatic beta cells, pancreatic acinar cells, pancreatic duct cells, vascular endothelial cells, left atrium, bladder, breast, cervix, colon, esophagus, oral cavity, kidney, prostate, rectum, stomach, thyroid, uterus, lung bronchial cells, cholangiocytes, muscle, oligodendrocytes, and ovary. Each possibility represents a separate embodiment of the invention. In some embodiments, the methylome atlas comprises methylation data from at least 1, 2, 3, 4, 5, 10, 15, 20, 25, 29, 30, 31, 32, 33 or 34 of the following 34 tissues or cell types: monocytes, B-cells, CD4+ T-cells, NK-cells, CD8+ T-cells, eosinophils, neutrophils, erythrocyte progenitors, adipocytes, neurons, hepatocytes, lung alveolar cells, pancreatic beta cells, pancreatic acinar cells, pancreatic duct cells, vascular endothelial cells, left atrium, bladder, breast, cervix, colon, esophagus, oral cavity, kidney, prostate, rectum, stomach, thyroid, uterus, lung bronchial cells, cholangiocytes, muscle, oligodendrocytes, and ovary. Each possibility represents a separate embodiment of the invention. In some embodiments, the methylome atlas comprises methylation data from at least 1, 2, 3, 4, 5, 10, 15, 20, 25, 30, 34 or 35 of the following 35 tissues or cell types: monocytes, B-cells, CD4+ T-cells, NK-cells, CD8+ T-cells, eosinophils, neutrophils, erythrocyte progenitors, adipocytes, neurons, hepatocytes, lung alveolar cells, pancreatic beta cells, pancreatic acinar cells, pancreatic duct cells, vascular endothelial cells, left atrium, bladder, breast, cervix, colon, esophagus, oral cavity, head and neck, kidney, prostate, rectum, stomach, thyroid, uterus, lung bronchial cells, cholangiocytes, muscle, oligodendrocytes, and ovary. Each possibility represents a separate embodiment of the invention. In some embodiments, the methylome atlas comprises methylation data from at least 1, 2, 3, 4, 5, 10, 15, 20, 25, 29, 30, 31, 32, 33, 34 or 35 of the following 35 tissues or cell types: monocytes, B-cells, CD4+ T-cells, NK-cells, CD8+ T-cells, eosinophils, neutrophils, erythrocyte progenitors, adipocytes, neurons, hepatocytes, lung alveolar cells, pancreatic beta cells, pancreatic acinar cells, pancreatic duct cells, vascular endothelial cells, left atrium, bladder, breast, cervix, colon, esophagus, oral cavity, head and neck, kidney, prostate, rectum, stomach, thyroid, uterus, lung bronchial cells, cholangiocytes, muscle, oligodendrocytes, and ovary. Each possibility represents a separate embodiment of the invention. In some embodiments, the monocytes are CD14+ monocytes. In some embodiments, the B-cells are CD19+ B-cells. In some embodiments, the NK-cells are CD56+ NK-cells. In some embodiments, oral cavity cells are head and neck cells.

In some embodiments, the atlas comprises at least the 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 100, 200, 300, 400, 500, 1000, 2000, 3000, 4000 or 5000 most uniquely methylated sites in a tissue or in each tissue. Each possibility represents a separate embodiment of the invention. In some embodiments, the atlas comprises at least 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, or 100 of the 100 most uniquely methylated sites in a tissue or in each. Each possibility represents a separate embodiment of the invention. In some embodiments, the atlas comprises at least the 100 most uniquely methylated sites in a tissue. In some embodiments, the methylome atlas further comprises any CpG sites within at least 50, 100, 150, 200, 250, 300, 400, 500, 1000, 1500, or 2000 base pairs upstream and/or downstream of the most uniquely methylated sites in a tissue or in each tissue. Each possibility represents a separate embodiment of the invention.

In some embodiments, the atlas comprises at least the 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 100, 200, 300, 400, 500, 1000, 2000, 3000, 4000 or 5000 most uniquely unmethylated sites in a tissue or in each tissue. Each possibility represents a separate embodiment of the invention. In some embodiments, the atlas comprises at least 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, or 100 of the 100 most uniquely unmethylated sites in a tissue or in each tissue. Each possibility represents a separate embodiment of the invention. In some embodiments, the atlas comprises at least the 100 most uniquely unmethylated sites in a tissue. In some embodiments, the methylome atlas further comprises any CpG sites within at least 50, 100, 150, 200, 250, 300, 400, 500, 1000, 1500, or 2000 base pairs upstream and/or downstream of the most uniquely unmethylated sites in a tissue or in each tissue. Each possibility represents a separate embodiment of the invention.

Selection of hyper/hypomethylated sites can be performed as follows: For finding cell-type specific CpGs, after obtaining a matrix X of N CpGs by K cell types, where each column represents the average methylation of all samples of that cell type at each CpG, a matrix X′ is then created, where each row X_(i)′ was calculated as

$X_{i}^{\prime} = {X_{i} \times {\frac{1}{{\sum_{k}^{K}X_{i,k}}\;}.}}$

For each cell type k, the rows with the highest value in column k as uniquely hyper-methylated CpGs can be identified. To identify uniquely hypomethylated CpGs, a similar process is performed: a matrix X″=1−X was defined, then a matrix

$X_{i}^{\prime\prime\prime} = {X_{i}^{\prime\prime} \times \frac{1}{\sum_{k}^{K}X_{i,k}^{\prime\prime}}}$

was defined and again the rows with the highest value in column k as uniquely hypomethylated CpGs were identified. For each cell type, the top hypomethylated CpGs and unmethylated CpGs can be included in the reference matrix. A more detail description of an algorithm that may be used for site selection follows.

Optionally, an efficient algorithm for selecting a small number of CpGs for inference of cell-of-origin for circulating cell-free DNA using DNA methylation patterns and Compressed Sensing.

Given an expression atlas X, composed of n CpGs over d cell types, a matrix A will denote a normalized version of the matrix X For rows Xi whose average methylation <0.5 we will set Ai=Xi, and set A1=1−Xi for all other rows. Next, we will normalize each row Ai to have a Euclidean norm of one: Ai=Ai/∥Ai∥. We are now ready to set the Compressed Sensing problem. Given a matrix A, we wish to find a column-sparse matrix W such that W×A is approximately the identity matrix, and:

$\min\limits_{w}\left( {{{{{WA} - I}}^{2}\mspace{14mu} {such}\mspace{11mu} {that}_{\mspace{14mu}}{{1^{T}W}}_{0}} \leq s} \right.$

where s is the number of non-zero columns in W.

Once W is found such that W×A≈I, for every vector Y=Aβ that is a linear combination of columns in A, it applies that WY=W(Aβ)=(WA)β=Iβ=β or in other words, multiplying a mixed vector Y by W would immediately result in the mixture coefficients β.

Optionally, a greedy algorithm is used for finding W. First, assume that a set S of the CpGs (|S|=s) is provided. Given such a partial set S, a projection of W to the S columns is denoted by Ws, and a projection of A to the S rows is denoted by As.

$W_{S} = {\arg \; {\min\limits_{B}{{{BA}_{S} - I}}^{2}}}$

Thus, finding the non-zeros columns of W, denoted Ws, is equivalent to finding a matrix B which is the pseudo-inverse of the S rows of A, and may be achieved with standard tools (e.g. the pinv function in MATLAB or numpy.linalg.pinv in Python). Next, the set S may be increased by adding the next, i'th, feature that minimizes:

$i = {\arg \; {\min\limits_{i \notin S}\mspace{14mu} {\min\limits_{B}{{{BA}_{S + {\{ i\}}} - I}}^{2}}}}$

This is equivalent to using the current base Ws, and finding a new orthogonal vector v, such that:

$\begin{matrix} {i = {\arg \; {\min\limits_{i \notin S}\mspace{14mu} {\min\limits_{v}{{{v\; A_{i}} + {W_{S}A_{S}} - I}}^{2}}}}} & {{EQN}\mspace{14mu} 1} \end{matrix}$

Let one define the matrix C=I−WsAs and a function ƒ(v)=∥vAi−C∥². By deriving ƒ with respect to v, one may find the optimal value v=CAi/∥Ai∥² for each i. Next, one may search for the next feature i minimizing ƒ(v). By substituting v in EQN 1 and some manipulations, one obtains:

$i = {\arg \; {\max\limits_{i}{{A_{i}C^{T}}}^{2}}}$

where the n×d matrix AC^(T) may be computed and the row i chosen with the maximal L₂ norm.

By repeating this procedure d times, with d denoting the number of cell types in the atlas, and the results given that the rank of A>d, an optimal subset of d CpG sites may be found that reveal the mixture coefficients β.

To obtain a more robust solution, this procedure may be repeated J times (each time, the (j) is used after excluding all features S from previous (j) runs), yielding several sparse matrices W. By averaging over these matrices W′=E[W], a sparse W′ matrix may be obtained which is defined over a superset of |S×J| CpG features. The multiplication of the W′ matrix with any mixture data Y may robustly estimate the mixture coefficient β.

Due to measurement noise, this approach may return erroneous results from specific samples of very similar cell types. Such cell types may have very similar methylation patterns, with few separating CpGs. To correct the erroneous results, one may choose to focus on a subset of CpG sites using a feature selection approach, that would construct an “Error-correcting code” as follows.

Another set S may explicitly include CpGs that are differentially methylated between overall similar cell types. Given a current set S of CpG sutes, one could consider the distance between all pairs of cell types <i,j> when projected onto the current set S of CpGs, and identifying the most similar pair <i,j>. Then, one can identify the CpG site <k> that is the most differentially methylated among cell types i and j, and add the CpG <k> into the set S. By repeating this process iteratively, one can identify a large set of CpGs S whose methylation pattern may differentiate between similar tissues. Namely, at each stage, one would identify the most similar pair of cell types/tissues <i,j> given the currnet set 5,

$\arg \; {\max\limits_{i,j}{{X_{i} - X_{j}}}_{S}^{2}}$

and then find the k'th feature that would further separate them the most:

${\arg \; {\max\limits_{k}\left( {{{X_{i} - X_{j}}}_{S + {\{ k\}}}^{2} - {{X_{i} - X_{j}}}_{S}^{2}} \right)}} = {\arg \; {\max\limits_{k}{{X_{i} - X_{j}}}_{\{ k\}}^{2}}}$

Pairwise distances may be computed among all cell types and used to separate the current pair of tissues. This procedure may be iteratively applied until all pairs of cell types are differentiated.

A representative example of CpG selection using the Compressed Sensing algorithm can be found in FIG. 1. CpGs can be selected using one round of the algorithm (FIG. 1A), or by repeated rounds (FIG. 1B). After that the error correcting code can be applied for as many iterations as are required (FIG. 1C). This method greatly improves the difference between similar tissues (FIG. 1D), as there are a limited number of informative CpGs sites that can be added to the atlas. (FIG. 1E). For example, a two-step feature selection algorithm may be used for identifying informative CpGs in a DNA methylation atlas. This algorithm may be extremely fast, scanning >450K CpGs in less than a minute, to identify a subset of CpGs and to compute a matrix W whose multiplication by any (mixed) plasma sample will result in an accurate estimation of the admixture parameters.

This approach may be scalable and applied to much larger datasets, covering hundreds or thousands of tissue types across all 30M CpGs. Thus, it may be suitable for analyzing whole-genome bisulfite-seq data, e.g. for cell-of-origin identification. In addition, it may be applied to identify the most informative regions (or CpG blocks) along the genome, thus used for designing efficient targeted applications (e.g. capture-based). The technique allows to explicitly focus on pairs of similar cell types that are prone to be confused by other methods, thus identifying a set of key CpGs for accurate and robust analysis of cell-free DNA methylation data.

In some embodiments, the site selection is performed using a latent probabilistic model applied to cell free DNA methylation bisulfite sequencing data.

For example, a latent probabilistic model is used for the analysis of bisulfite sequencing DNA methylation data to infer the cell type and tissue type composition of cell free DNA (tissue of origin) and to quantitatively detect circulating tumor DNA in peripheral blood samples, while incorporating prior medical knowledge.

For example, circulating cell free DNA fragments in the peripheral blood may be analyzed to infer the quantitative admixture of tissues and specific cell types from which the cell fragments originated, and to detect small fractions of circulating tumor DNA fragments using CpGs methylation patterns along the genome. Disclosed herein is a computational model to analyze data from a whole genome, a reduced representation, a capture-based method, or the like, followed by bisulfite sequence (BS-seq) determination on DNA fragments originating from the plasma of peripheral blood samples of human patients.

The probabilistic model infers the relative amounts (in genomes/ml units) of tissue-specific and tumor-specific DNA fragments found in plasma samples. A unified probabilistic model, whose (latent) parameters are composed of (a) the admixture coefficient θ, and (b) the tissue-specific and tumor-specific statistical models of CpG methylation patterns. A Bayesian or Maximum Likelihood estimations are applied to infer the latent parameters thus quantitatively identifying the admixture contributions and/or to estimate statistical confidence intervals for each admixture coefficient θ and/or to infer the estimated probability of the θ>0 for each healthy or pathological cell type.

For example, consider bisulfite sequencing data D, composed of multiple sequenced reads (or fragments) f. The likelihood of the data D given the model parameters θ can be written as:

${\mathcal{L}\left( {\theta \text{:}D} \right)} = {\prod\limits_{f \in D}{p(f)}}$

where the likelihood of observing a read f is written as p(f) and assuming independence between sequenced reads.

The reads may be sampled from (an unobserved) composition of multiple types t=1 . . . T of methylation patterns, each from a different cell type, tumor-type, or some cellular condition. Using the law of total probability, we can decompose p(f) as a weighted sum of conditional probabilities p_(t) (f) for each cell type t:

${{\mathcal{L}\left( {\theta \text{:}D} \right)} - {\prod\limits_{f \in D}{\sum\limits_{t \in T}{{p(t)}{p\left( f \middle| t \right)}}}}} = {\prod\limits_{f \in D}\; {\sum\limits_{t \in T}{\theta_{t}{p_{t}(f)}}}}$

where p(t)=θ_(t) represents the relative amount of cell-free DNA fragments in the plasma from cell-type or tumor-type t and p(f|t)=p_(t) (f) denotes the conditional probability of observing the sequenced read, f in cell-free DNA that has originated from cell type t.

The parameters of this model, including the admixture parameters θ_(t) and the cell type specific likelihood model p_(t) (f) are unobserved. Following is described how the likelihood is optimized with regard to θ.

An unknown distribution p_(t) (f) of observing a read f from the plasma portion contributed by cell type t. One can approximate p_(t)(f) using a methylation atlas, containing bisulfite sequencing data from purified cell types and tumor biopsies. Such data, from either whole genome bisulfite sequencing or from reduced representation bisulfate sequencing, may be rapidly accumulated and available for multiple cell types and tissues (healthy or pathological). In addition, some distributions p_(t) (f) may be estimated from other types of CpG methylation data, such as Illumina Infinium 450K or EPIC BeadChip platforms, which may be available for multiple cell types. Statistical correlations between adjacent CpGs in the same CpG haplotype blocks may be used for approximating the joint probability of CpG methylation in a genome wide manner.

The probability of multiple adjacent CpGs may be approximated using probabilistic graphical models that decompose the joint probability of multiple CpGs into compact models with few parameters by assuming conditional independencies (e.g. Markovian mathematical properties). For example, each CpG haplotype block i that contains up to hundred CpGs can be modeled in each cell type t using at least two parameters: β_(ti) that denotes the average methylation of CpGs within this block, and τ_(ti) that denotes the probability of two adjacent CpG being correlated (i.e., similarly methylated in a DNA molecule). This model may allow decomposing the likelihood of a given observation. For example, the likelihood of a read from the i'th block in the t'th cell type, with a CpG methylation pattern of TCCCTTT (C=methylated CpG, T=unmethylated CpG, with other nucleotides ignored) may be determined with the equation:

P(TCCCTTTT|θ_(t) _(i) )=β_(t) _(i) ³(1−β_(t) _(i) )⁴τ_(t) _(i) ⁴(1−τ_(t) _(i) )²

where the read contains three methylated CpGs (each with probability β_(ti)), four unmethylated CpG (each with probability 1−β_(ti)), four consecutive pairs of equally methylated CpGs (each, at probability m) and two consecutive pairs with alternating methylation (each with probability 1−τ_(ti)).

Optionally, given bisulfite sequencing data from cell type t, Maximum Likelihood models are used to infer the values β_(ti) and τ_(ti) for each cell type t and haplotype block i using:

${\hat{\beta}}_{t_{i}} = \frac{\sum_{f}{\sum_{j \in i}{1\left\{ {f_{j}\mspace{14mu} {is}\mspace{14mu} {methylated}\mspace{14mu} {in}\mspace{14mu} t} \right\}}}}{\sum_{f}{\sum_{j \in i}{1\left\{ \; \right\}}}}$ and ${\hat{\tau}}_{t_{i}} = \frac{\sum_{f}{\sum_{{{j_{,}j} + 1} \in i}{1\left\{ {f_{j} = f_{j + 1}} \right\}}}}{\sum_{f}{\sum_{j,{{j + 1} \in i}}{1\left\{ \; \right\}}}}$

where 1{ } denotes the indicator function, and f_(j) denotes the j'th CpG sequenced in the read f.

Using Bayes' theorem, one can infer for each read f, the posterior probability of it originating from the cell type t:

${P\left( t \middle| f \right)} = \frac{{P\left( f \middle| \theta_{t_{i}} \right)}{P\left( t_{i} \right)}}{\sum_{k}{{P\left( f \middle| \theta_{k_{i}} \right)}{P\left( k_{i} \right)}}}$

Here, P(t_(i)) denotes modeling the a-priori probability of observing a read from the i'th block, originating from the t'th cell type. This may allow one to model a non-uniform coverage of reads circulating in the plasma, in a tissue-specific manner, and more importantly, allows for the integration of prior patient-specific medical data into the computational model.

These inferred posterior probabilities of the cell-of-origin of each observed read, may play a role in detecting small amounts of circulating tumor cell free DNA fragments (ctDNA). Similarly, they may be integrated for the identification and quantification of circulating DNA from various sources (e.g. pathological or normal), including the estimations of confidence intervals.

More generally, a similar approach may infer the admixture coefficient p(t) of the entire circulating DNA in the plasma—finding a Maximum Likelihood solution to the deconvolution problem. Optionally this may be done by maximizing the likelihood of the data D, with subject to the p(t) using an Expectation Maximization algorithm, which iteratively calculates the expected probability of assigning each read to each originating tissue (E-step) and then computes the Maximum Likelihood estimation (or Bayesian estimation, given some medical prior knowledge) for each cell type that contributed DNA to the plasma.

This model may not be limited to whole genome bisulfite sequencing data and may be applied to reduced representation bisulfite sequencing data, or to capture-based bisulfite sequencing data. Moreover, for scalability and speed up some feature selection procedures may be applied prior to applying this model, thus focusing the model on the informative portions of the data and possibly ignoring sequenced reads that originate from other regions of the genome.

In some embodiments, the 100 most uniquely methylated sites are selected from Table 1. In some embodiments, the 100 most uniquely unmethylated sites are selected from Table 2. In some embodiments, the CpG sites within at least 150 base pairs upstream and downstream of the most uniquely methylated sites are selected from Table 1. In some embodiments, the CpG sites within at least 150 base pairs upstream and downstream of the most uniquely unmethylated sites are selected from Table 2.

In some embodiments, the methylome atlas further comprises at least one of the 500 CpG sites that best differentiate between the most similar pairs of tissues and cell types. In some embodiments, the analysis of which are the 500 best CpGs is performed iteratively, such that a new decision of which pair of tissues or cell types is most similar is made after each new CpG is added. In some embodiments, at least 1, 5, 10, 50, 100, 150, 200, 250, 300, 350, 400, 450, or 500 CpG sites that best differentiate are added to the atlas. One skilled in the art will understand that with greater numbers of tissues/cell types in the atlas, and with more similar tissues/cell types most of these informative CpGs may be added.

Iterative selection of CpGs that differentiate between the most similar pairs of tissues/cell types can be performed as follows in order to correct for very similar methylomes. Starting with a set S of CpGs (as previously described), the Euclidian distance over S was calculated for every pair of cell types <j,k> as

$\sqrt[2]{\sum_{i \in S}\left( {X_{i,j} - X_{i,k}} \right)^{2}},$

and the most similar pair <j′,k′> was identified. Then, the row with the highest difference in values in columns j′ and k′ can be identified, and added into the set S. In some embodiments, the 500 CpG sites that best differentiate between the most similar pairs of tissues and/or cell types are selected from Table 3. More details of algorithms that may be used for this correction are found herein.

In some embodiments the methods of the invention can be used to determine the origin of cfDNA even when the cfDNA from one tissue/cell types is a very small percentage of the whole cfDNA. In some embodiments, the cfDNA of a tissue and/or cell type comprises as little 0.5%, 1%, 1.5%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9% or 10% of all of the cfDNA. Each possibility represents a separate embodiment of the invention. In some embodiments, the cfDNA of a tissue and/or cell type comprises more than 0.5%, 1%, 1.5%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9% or 10% of all of the cfDNA. Each possibility represents a separate embodiment of the invention. In some embodiments, the cfDNA of a tissue and/or cell type comprises less than 0.5%, 1%, 1.5%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9% or 10% of all of the cfDNA. Each possibility represents a separate embodiment of the invention. In some embodiments, the cfDNA of a tissue and/or cell type comprises between 0.5%-10%, 1%-10%, 1.5%-10%, 2%-10%, 0.5%-9%, 1%-9%, 1.5%-9%, 2%-9%, 0.5%-8%, 1%-8%, 1.5%-8%, 2%-8%, 0.5%-7%, 1%-7%, 1.5%-7%, 2%-7%, 0.5%-6%, 1%-6%, 1.5%-6%, 2%-6%, 0.5%-5%, 1%-5%, 1.5%-5%, 2%-5%, 0.5%-4%, 1%-4%, 1.5%-4%, 2%-4%, 0.5%-3%, 1%-3%, 1.5%-3%, 2%-3%, 0.5%-2%, 1%-2%, 1.5%-2%, or 0.5%-1.5% of all of the cfDNA. Each possibility represents a separate embodiment of the invention.

In some embodiments, the methods of the invention determine cfDNA is from a tissue. In some embodiments, the methods of the invention determine cfDNA is from a cell type. In some embodiments, the methods of the invention determine cfDNA is from a tissue and/or a cell type. In some embodiments, cfDNA may be determined to come from more than one cell type of a tissue. In some embodiments, the presence of cfDNA from a cell type or cell types may be used to determine the cfDNA is from the tissue from which the cell type is derived. It will be understood by one skilled in the art that the specificity of the methylation marks defines the specificity of the result. If a beta-cell mark is elevated, it means beta-cells died. It says nothing about other cell types inside or outside the pancreas. If markers from the whole pancreas are elevated, or markers from multiple pancreatic cell types are elected, it means pan-pancreatic damage. Thus, if only one cell type is elevated it means selective damage to that cell type.

In some embodiments, the methods of the invention are for use in detecting a disease state or condition in a subject in need thereof and wherein the cfDNA is from the subject. In some embodiments, the methods of the invention are for diagnosing a disease, and/or condition in a subject in need thereof and wherein the cfDNA is from the subject. In some embodiments, the methods of the invention are for diagnosing an increased risk of a disease or condition. A skilled artisan will recognize that many if not all disease states induce cell death in the tissue or cells in which the disease is manifest. As such knowledge of the origin of cell death be a surrogate for the disease. In some embodiments, the disease state or condition is selected from organ transplantation, sepsis, and cancer. In some embodiments, the disease state or condition is selected from organ transplantation, sepsis, cancer, neurodegenerative disease, degenerative disease, infection, inflammatory disease, toxicity, trauma, hypoxia, vascular disease and metabolic stress. In some embodiments, the disease is cancer and the methods of the invention determine the cell or tissue of origin of the cancer.

By another aspect, there is provided a computer program product for determining the cell or tissue of origin of cell free DNA (cfDNA), comprising a non-transitory computer-readable storage medium having program code embodied thereon, the program code executable by at least one hardware processor to:

-   -   a. measure or access measurements of DNA methylation of cfDNA;     -   b. assign a cfDNA molecule from said cfDNA to a cell type or         tissue of origin by comparing the methylation of said molecule         to a methylome atlas; and     -   c. provide an output regarding the cell or tissue of origin of         cfDNA.

By another aspect, there is provided a computer program product for determining the cell or tissue of origin of cell free DNA (cfDNA), comprising a non-transitory computer-readable storage medium having program code embodied thereon. The program code is executable by at least one hardware processor to:

-   -   a. receive a methylation sequencing data acquired from a cfDNA         sample and a methylome atlas. The methylome atlas comprises (i)         a first set of uniquely methylated sites, (ii) a plurality of         neighboring methylated sites, and (iii) a second set of uniquely         methylated sites. At least some of the neighboring methylated         sites may be within 500 base units to each element of the first         set of uniquely methylated sites. The second set of uniquely         methylated sites comprises more than double the number of sites         of the first set and comprising less than half the number of         tissue types in each comparison; In this manner the second set         refines the highest ranking or highest probability tissue types,         to narrow down the search to one or more final candidates.     -   b. compare the methylation sequencing data with a methylome         atlas to determine a set of candidate tissues. The comparing         comprises identifying at least some elements of the first set of         uniquely methylated sites in both the methylation sequencing         data and the methylome atlas.     -   c. compare the plurality of neighboring methylated sites of the         methylation sequencing data with the atlas entries of the set of         candidate tissues to determine a first subset of candidate         tissues.     -   d. compare the second set of uniquely methylated sites of the         methylome atlas within the methylation sequencing data to         determine a probability ranking of candidate tissues.     -   e. assign at least one of the highest-ranking tissues of the         probability ranking of candidate tissues to the cfDNA sample.

For example, an atlas of uniquely methylated sites has different scales of differentiation. A first scale may be each tissue type against all other tissue types. A second scale may be one tissue type compared to a few tissue types, such as between 1 and 10 other tissue types. An associated set of neighboring sites may be used on the first or second sets along to better determine similar tissues. For example, there may be k-levels of scales for differentiating between tissue types, such as differentiating between different genetic families or the like. For example, each comparison scale atlas data may be considered a subset of the full comparison atlas with N to N comparisons. For example, a series of rules and comparison subsets may allow differentiating tissues with greater than 95% accuracy, greater than 97.5% accuracy, greater than 98.5% accuracy, greater than 99.5% accuracy, and/or the like.

By another aspect, there is provided a computerized system for determining the cell or tissue of origin of cfDNA, comprising:

-   a. one or more test devices for measuring DNA methylation of cfDNA; -   b. a processor; and -   c. storage medium comprising a computer application that, when     executed by the processor, is configured to:     -   i. measure or access measurements of DNA methylation of cfDNA;     -   ii. assign a cfDNA molecule from said cfDNA to a cell type or         tissue of origin by comparing the methylation of said molecule         to a methylome atlas; and     -   iii. output from the processor the cell or tissue of origin of         cfDNA.

By another aspect, there is provided a computerized system for determining the cell or tissue of origin of cfDNA, comprising: (i) at least one hardware processor; and (ii) a non-transitory computer-readable storage medium having program code embodied thereon. The program code executable by the at least one hardware processor to:

-   -   a. receive a methylation sequencing data acquired from a cfDNA         sample and a methylome atlas, wherein the methylome atlas         comprises (i) a first set of uniquely methylated sites, (ii) a         plurality of neighboring methylated sites, and (iii) a second         set of uniquely methylated sites comprising more than double the         number of sites of the first set. The second set of uniquely         methylated sites comprises less than half the number of tissue         types in each comparison as the first set. At least some of the         neighboring methylated sites are within 500 base units to each         element of the first set of uniquely methylated sites.     -   b. compare the methylation sequencing data with a methylome         atlas to determine a set of candidate tissues, wherein the         comparing comprises identifying at least some elements of the         first set of uniquely methylated sites in both the methylation         sequencing data and the methylome atlas.     -   c. compare the plurality of neighboring methylated sites of the         methylation sequencing data with the atlas entries of the set of         candidate tissues to determine a first subset of candidate         tissues.     -   d. compare the second set of uniquely methylated sites of the         methylome atlas within the methylation sequencing data to         determine a probability ranking of candidate tissues.     -   e. assign at least one of the highest-ranking tissues of the         probability ranking of candidate tissues to the cfDNA sample.

In some embodiments, the first set comprises between 25 and 100 most uniquely methylated sites. For example, the first subset is a wide scale search for similar tissue types, but may not differentiate to specific tissue types.

In some embodiments, the first set comprises a plurality of most uniquely methylated sites and wherein the plurality of neighboring methylated sites comprises any CpG sites within between 150 and 500 base pairs upstream and downstream of said most uniquely methylated sites in each tissue or cell type. For example, the neighboring methylated sites are the patterns of methylated sites surrounding the uniquely methylated sites, such as within a window difference from each uniquely methylated site, such as a fixed base unit distances, and/or the like.

In some embodiments, the second set comprises between 100 and 500 most uniquely methylated sites. For example, the second subset is a limited list of comparisons (i.e. pairs, triplets, quadruples, etc.) that would fully differentiate a tissue sample data from similar tissue types (i.e. similar uniquely methylated sites).

In some embodiments, the second set of uniquely methylated sites compares a plurality of specific pairs or triplets of tissue types, such as similar tissue types, or the like.

In some embodiments, the first set of uniquely methylated sites are uniquely methylated as compared to all cell types and tissues of the atlas, and wherein the second set of uniquely methylated sites are uniquely methylated in one cell type or tissue as compared to a second most similar cell type or tissue. This example of multiscale genetic searching may allow quick determination of a sample origin and possible pathologies from a minimal sized atlas. The benefits of a small atlas are easier updates, distribution, and/or the like.

In some embodiments, the comparing comprises using a latent probabilistic model. For example, multiple models may be used to determine the highest probability tissue types.

In some embodiments, the methylome atlas is of at least 5 cell types or tissues, wherein said atlas comprises at least 25 of the 100 most uniquely methylated sites and at least 25 of the 100 most uniquely unmethylated sites in each of said 5 cell types or tissues.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

These computer readable program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

Embodiments may comprise a computer program that embodies the functions described and illustrated herein, wherein the computer program is implemented in a computer system that comprises instructions stored in a machine-readable medium and a processor that executes the instructions. However, it should be apparent that there could be many different ways of implementing embodiments in computer programming, and the embodiments should not be construed as limited to any one set of computer program instructions. Further, a skilled programmer would be able to write such a computer program to implement one or more of the disclosed embodiments described herein. Therefore, disclosure of a particular set of program code instructions is not considered necessary for an adequate understanding of how to make and use embodiments. Further, those skilled in the art will appreciate that one or more aspects of embodiments described herein may be performed by hardware, software, or a combination thereof, as may be embodied in one or more computing systems. Moreover, any reference to an act being performed by a computer should not be construed as being performed by a single computer as more than one computer may perform the act.

By testing device is meant a combination of components that allows the methylation of a piece of DNA to be determined. In some embodiments, the testing device allows for the high-throughput determinization of DNA methylation. The components may include any of those described above with respect to the methods for determining DNA methylation. For example, the components may be bisulfate conversion kits, or Illumina methylation arrays, and so on.

In certain embodiments the system or test kit further comprises a display for the output from the processor.

By another aspect, there is provided a method of constructing a methylome atlas, comprising:

-   -   a. providing DNA methylation data from at least 5 tissues and/or         cell types;     -   b. selecting at least the top 25 uniquely hyper-methylated and         at least the top 25 uniquely hypomethylated CpG sites per a         tissue and/or cell type as compared to the other tissues and/or         cell types; and     -   c. combining the uniquely hyper-methylated and uniquely         hypo-methylated sites for all tissues and/or cell types;     -   thereby constructing a methylome atlas.

In some embodiments, the methods of the invention further comprise:

-   -   d. selecting all neighboring CpG sites within at least 150 base         pairs upstream and/or downstream of each uniquely         hyper-methylated and uniquely hypomethylated CpG site; and     -   e. combining the neighboring CpG sites with the uniquely         hyper-methylated and uniquely hypomethylated CpG sites.

In some embodiments, the methods of the invention further comprise:

-   -   f. selecting at least the top 25 CpG sites that best         differentiate between the most similar pair of tissues and/or         cell types; and     -   g. combining the CpG sites that best differentiate with the         uniquely hyper-methylated and uniquely hypomethylated CpG sites.

In some embodiments, the atlas is constructed only with data from whole tissues. In some embodiments, the atlas is constructed only with data from purified cell populations. In some embodiments, the atlas is constructed from data from both tissues and cell populations. As used here, the term “cell type” refers to a unique cell population. Cell types are generally defined by marker that identified the population. This marker can be a genetic marker, or protein expression or morphological to give a few non-limiting examples. Separating cell populations is well known in the art, and can be performed, for example, with magnetic beads, by gradient separation, or by FACS sorting.

In some embodiments, the atlas is constructed with data from cell types from tissue. In some embodiments, the cell types are purified populations from a tissue or organ. In some embodiments, the atlas comprises at least 2 purified populations from the same tissue. In some embodiments, the atlas is constructed with data from purified populations of blood derived cells and tissue derived cells. In some embodiments, the atlas is constructed only from blood derived cells or only tissue derived cells.

In some embodiments, the DNA methylation data is genome wide data. In some embodiments, the DNA methylation data is from a part of the genome. In some embodiments, the DNA methylation data is at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% or 90% of all CpG sites in the genome. Each possibility represents a separate embodiment of the invention. In some embodiments, the DNA methylation data is from a DNA methylation chip or array. Methylation chips, such as for example the Illumina Infinium Human Methylation 450K Beadchip array and the Infinium Human Methylation EPIC Beadchip array, are well known in the art and may be used to provide DNA methylation data for the methods of the invention. In some embodiments, the DNA methylation data is from at least 100000, 150000, 200000, 250000, 30000, 350000, 40000, 450000, or 500000 genomic loci. Each possibility represents a separate embodiment of the invention.

In some embodiments, the method comprises selecting at least the top 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 100, 200, 300, 400, 500, 1000, 2000, 3000, 4000 or 5000 most uniquely methylated sites in a tissue or in each tissue. Each possibility represents a separate embodiment of the invention. In some embodiments, the method comprises selecting at least 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, or 100 of the top 100 most uniquely methylated sites in a tissue or in each tissue. Each possibility represents a separate embodiment of the invention. In some embodiments, the method comprises selecting at least the top 100 most uniquely methylated sites in a tissue. In some embodiments, the method further comprises selecting any CpG sites within at least 50, 100, 150, 200, 250, 300, 400, 500, 1000, 1500, or 2000 base pairs upstream and/or downstream of the most uniquely methylated sites in a tissue or in each tissue. Each possibility represents a separate embodiment of the invention.

In some embodiments, the method comprises selecting at least the top 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 100, 200, 300, 400, 500, 1000, 2000, 3000, 4000 or 5000 most uniquely unmethylated sites in a tissue or in each tissue. Each possibility represents a separate embodiment of the invention. In some embodiments, the method comprises selecting at least 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, or 100 of the top 100 most uniquely unmethylated sites in a tissue or in each tissue. Each possibility represents a separate embodiment of the invention. In some embodiments, the method comprises selecting at least the 100 most uniquely unmethylated sites in a tissue. In some embodiments, the method further comprises selecting any CpG sites within at least 50, 100, 150, 200, 250, 300, 400, 500, 1000, 1500, or 2000 base pairs upstream and/or downstream of the most uniquely unmethylated sites in a tissue or in each tissue. Each possibility represents a separate embodiment of the invention.

In some embodiments, the method further comprises selecting at least the top 5, 10, 20, 30, 40, 50, 75, 100, 150, 200, 250, 300, 350, 400, 450, or 500 CpG sites that best differentiate between the most similar pairs of tissues and/or cell types. Each possibility represents a separate embodiment of the invention. In some embodiments, the analysis of which are the best CpGs is performed iteratively, such that a new decision of which pair of tissues or cell types is most similar is made after each new CpG is added. One skilled in the art will understand that with greater numbers of tissues/cell types in the atlas, and with more similar tissues/cell types most of these informative CpGs may be added.

In some embodiments, the DNA methylation data has been preprocessed to remove unreliable CpG sites. In some embodiments, an unreliable site is a site with less than 3 beads. In some embodiments, an unreliable site has a P-value representing the total fluorescence of the relevant probes that is below 0.01 or 0.05. In some embodiments, an unreliable site has a median absolute error of below 0.05.

As used herein, the term “about” when combined with a value refers to plus and minus 10% of the reference value. For example, a length of about 1000 nanometers (nm) refers to a length of 1000 nm+−100 nm.

It is noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a polynucleotide” includes a plurality of such polynucleotides and reference to “the polypeptide” includes reference to one or more polypeptides and equivalents thereof known to those skilled in the art, and so forth. It is further noted that the claims may be drafted to exclude any optional element. As such, this statement is intended to serve as antecedent basis for use of such exclusive terminology as “solely,” “only” and the like in connection with the recitation of claim elements, or use of a “negative” limitation.

In those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.”

It is appreciated that certain features of the invention, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the invention, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub-combination. All combinations of the embodiments pertaining to the invention are specifically embraced by the present invention and are disclosed herein just as if each and every combination was individually and explicitly disclosed. In addition, all sub-combinations of the various embodiments and elements thereof are also specifically embraced by the present invention and are disclosed herein just as if each and every such sub-combination was individually and explicitly disclosed herein.

Additional objects, advantages, and novel features of the present invention will become apparent to one ordinarily skilled in the art upon examination of the following examples, which are not intended to be limiting. Additionally, each of the various embodiments and aspects of the present invention as delineated hereinabove and as claimed in the claims section below finds experimental support in the following examples.

Various embodiments and aspects of the present invention as delineated hereinabove and as claimed in the claims section below find experimental support in the following examples.

EXAMPLES

Generally, the nomenclature used herein, and the laboratory procedures utilized in the present invention include molecular, biochemical, microbiological and recombinant DNA techniques. Such techniques are thoroughly explained in the literature. See, for example, “Molecular Cloning: A laboratory Manual” Sambrook et al., (1989); “Current Protocols in Molecular Biology” Volumes I-III Ausubel, R. M., ed. (1994); Ausubel et al., “Current Protocols in Molecular Biology”, John Wiley and Sons, Baltimore, Md. (1989); Perbal, “A Practical Guide to Molecular Cloning”, John Wiley & Sons, New York (1988); Watson et al., “Recombinant DNA”, Scientific American Books, New York; Birren et al. (eds) “Genome Analysis: A Laboratory Manual Series”, Vols. 1-4, Cold Spring Harbor Laboratory Press, New York (1998); methodologies as set forth in U.S. Pat. Nos. 4,666,828; 4,683,202; 4,801,531; 5,192,659 and 5,272,057; “Cell Biology: A Laboratory Handbook”, Volumes I-III Cellis, J. E., ed. (1994); “Culture of Animal Cells—A Manual of Basic Technique” by Freshney, Wiley-Liss, N. Y. (1994), Third Edition; “Current Protocols in Immunology” Volumes I-III Coligan J. E., ed. (1994); Stites et al. (eds), “Basic and Clinical Immunology” (8th Edition), Appleton & Lange, Norwalk, Conn. (1994); Mishell and Shiigi (eds), “Strategies for Protein Purification and Characterization—A Laboratory Course Manual” CSHL Press (1996); all of which are incorporated by reference. Other general references are provided throughout this document.

Materials and Methods Reference Matrix

All DNA methylation profiles were profiled either on the Illumina Infinium Human Methylation 450K Beadchip array or the Infinium Human Methylation EPIC Beadchip array. DNA methylation data for white blood cells (neutrophils, monocytes, eosinophils, B-cells, CD4+ T-cells, CD8+ T-cells, NK-cells, n=6 each) were downloaded from GSE35069 (450K). Data for erythrocyte progenitors (n=5) were downloaded from GSE63409 (450K), and data for left atrium (n=4) were downloaded from GSE62727 (450K). Data for bladder (n=19), breast (n=98), cervix (n=3), colon (n=38), esophagus (n=16), head and neck (n=34), kidney (n=160), prostate (n=50), rectum (n=7), stomach (n=2), thyroid (n=56) and uterus (n=34) were downloaded from TCGA (450K). DNA methylation data for adipocytes (n=3, 450K), hepatocytes (n=2, 450K and EPIC), alveolar lung cells (n=2, EPIC), neurons (n=1, 450K), vascular endothelial cells (n=2, EPIC) and pancreatic acinar cells (n=3, 450K (n=1) and EPIC (n=2)), duct cells (n=3, 450K (n=1) and EPIC (n=2)), beta cells (n=5, 450K (n=3) and EPIC (n=2)) were generated from samples. After construction of the atlas used in the experiment described hereinbelow, data was also compiled for lung bronchial cells, cholangiocytes, muscle, oligodendrocytes and ovary samples.

Cell Isolation

Cancer-free primary human tissue was obtained from consenting donors, dissociated to single cells, sorted using cell type-specific antibodies, and lysed to obtain genomic DNA, from which 250 ng were applied to an Illumina EPIC methylation array. Adipocytes, cortical neurons, hepatocytes, pancreatic acinar cells, pancreatic beta cells and duct cells were obtained from cadaveric donors, as was distal lung tissue. Alveolar epithelial cells were isolated from the lung by FACS using an antibody for EpCAM. Vascular endothelial cells were isolated using anti-CD31 magnetic beads from the saphenous vein, following surgically excision.

Blood Samples

Donors were consented and whole blood (usually 20 ml) was drawn, collected into an EDTA tube, and spun quickly to separate plasma, which was stored at −20 c until isolation of cfDNA.

DNA Extraction

250 ng was collected from each sample, except where otherwise specified. DNA concentration was measured with Qubit. cfDNA extraction from plasma was performed with the QIAsymphony liquid handling robot.

Data Processing

Methylation array data were processed with the minfi package in R. For each sample analyzed on the Illumina Methylation array, CpG sites were filtered out if they were represented by less than 3 beads on the array, if the detection P-value, representing total fluorescence of the relevant probes, was lower than 0.01 or if they mapped to a sex chromosome. Background correction and normalization were performed with the preprocess Illumina function, which removes background calculated based on internal control probes and normalizes all samples to a predetermined control sample.

CpG Selection

As the reference database included samples analyzed with two highly similar yet not identical platforms, the Illumina 450K array and the Illumina EPIC array, sites with low reproducibility between the platforms were identified and removed. To this end, data from samples analyzed on both platforms were collected: 15 samples from GSE86833, 12 samples from GSE92580, 1 sample from our generated dataset (hepatocytes). For each overlapping CpG, the median absolute error (MAE) between the 450K samples and the corresponding EPIC samples was calculated, and 37747 CpGs with a MAE<0.05 were removed.

For finding cell-type specific CpGs, after obtaining a matrix X of N cpgs by K cell types, where each column represents the average methylation of all samples of that cell type at each CpGs, there was then created a matrix X′ where each row X_(i)′ was calculated as

$X_{i}^{\prime} = {X_{i} \times {\frac{1}{\sum_{k}^{K}X_{i,k}}.}}$

For each cell type k, the 5000 rows with the highest value in column k as uniquely hypermethylated CpGs were identified. To identify uniquely hypomethylated CpGs, a similar process was performed: a matrix X″=1−X was defined, then a matrix

$X_{i}^{\prime\prime\prime} = {X_{i}^{\prime\prime} \times \frac{1}{\sum_{k}^{K}X_{i,k}^{\prime\prime}}}$

was defined and again the 5000 rows with the highest value in column k as uniquely hypomethylated CpGs were identified. For each cell type, the top 5000 hypomethylated CpGs and unmethylated CpGs were included in the reference matrix.

Deconvolution

To calculate the relative contribution of each cell type to a given sample, non-negative least squares were performed, as implemented in the nnls package in R. Given a matrix X of reference methylation values with N CpGs and K cell types, and a vector y of methylation values of length N, non-negative coefficients β′ were identified by solving argmin_(β)∥Xβ−y∥₂, subject to β≥0. The resulting β was adjusted to have a sum of 1, where for each β_(k) it was defined

$\beta_{k}^{\prime} = {\frac{\beta_{k}}{\sum_{k}^{K}\beta_{k}}.}$

to obtain absolute levels of cfDNA (genome equivalent/ml) per cell type, the resulting β_(k)′ was multiplied by the total concentration of cfDNA present in the sample, as measured by Qubit.

Simulations

4 leukocyte samples were profiled with Illumina methylation arrays (3 with 450K, 1 with EPIC). For each cell type, each available sample was mixed with each leukocyte sample in ratios 0%:100%, 0.1%:99.9%, 0.2%:99.8%, . . . , 100%:0%. Then for each cell type, at each ratio, the average predicted percentage was calculated as well as the 90% confidence interval.

Sample Pooling

Pooled DNA samples were obtained by mixing DNA from several individuals. DNA was extracted from 8 ml of plasma, and samples were added until 250 ng reached (5-10 samples per pool). No individual contributed more than 2 times as much DNA to a pool than another individual.

Results

Herein the ability to identify the cellular contributors to cfDNA in healthy and pathological conditions is evaluated in an unbiased manner, using an extensive reference atlas of human cell- and tissue-specific methylomes. The Illumina Infinium Methylation Array was used, which allows for the simultaneous analysis of the methylation status of >450,000 CpG sites throughout the human genome. Illumina methylation arrays have already been used in the deconvolution of whole blood methylation profiles to determine the relative proportions of white blood cells in a sample, a crucial step in Epigenome-Wide Association Studies (EWAS). However, to date, this deconvolution approach has been applied only to whole blood samples, where all contributing cells are well known types of white blood cells. By contrast, the plasma methylome presents major challenges. First, the Illumina arrays require 250-500 ng DNA, which in healthy individuals can be obtained from 100-200 ml blood, much above the standard in blood tests. Second, both the cellular sources and their relative contributions to cfDNA are not known, complicating the computational problem of accurate deconvolution. It was hypothesized that by generating a comprehensive database of methylation profiles of human tissues and cell types, it would be possible to deconvolute the methylation profiles of plasma-derived cfDNA, and hence to infer the cellular contributions to cfDNA from a wide range of cell types. The major tissues contributing to cfDNA of healthy individuals was determined, as well as in several pathologies known to involve an increase in circulating cfDNA: Organ transplantation, sepsis and cancer.

Example 1: Development of a DNA Methylation Atlas

To obtain a comprehensive DNA methylation database of human cell types, datasets which were previously published, either as part of The Cancer Genome Atlas (TCGA) (Weisenberger, 2014) or by individual groups that deposited data in the Gene Expression Omnibus (GEO) were employed. In selecting datasets to be included in the database, the following criteria were used: 1) use primary tissue sources, which have not been passaged in culture (reasoning that culture may change methylation patterns or alter the cellular composition of a mixed tissue, e.g. enrich for fibroblasts); 2) use the methylomes of healthy human tissues, which are expected to be universally conserved; and 3) whenever possible use methylomes of cell types, rather than whole tissues. It was reasoned that since all tissues are a composite of multiple cell types (e.g. different types of epithelial cells, blood, vasculature and fibroblasts), methylation signatures of minority populations might be difficult to identify, and unique signatures of the tissue might be masked by the methylome of stroma.

Unfortunately, other than isolated blood cell types, the vast majority of publicly available methylomes come from bulk tissue. Therefore, methylation profiles of key human cell types which had not been previously published were generated, and these were included in the database. Primary human adipocytes, cortical neurons, hepatocytes, lung alveolar cells, pancreatic beta cells, pancreatic acinar cells, pancreatic duct cells, and vascular endothelial cells were isolated. As detailed in the hereinabove Material and Methods, surgical samples from each tissue were enzymatically dissociated according to published protocols, stained with antibodies against the cell type of interest, and sorted using either flow cytometry (FACS) or magnetic beads (MACS). DNA from sorted cells was then prepared, and the methylome obtained using the Illumina 450 k or EPIC array platforms. The result of this effort was a human methylome reference atlas, composed of 29 tissues or cell types (FIG. 2A). Five more cell types were then added to the atlas at a later date, though the same methodology was used.

Example 2: Deconvoluting the Methylome of a Mixture to Determine Cell Type Composition

To deconvolute cfDNA methylation profiles, a subset of CpG sites in the genome which are differentially methylated among the cell types and tissues in the atlas were selected. Only a subset of the methylome was selected to be used for deconvolution based on several considerations. Most CpG sites represented in the Illumina arrays have a similar methylation patters across all tissues; as a result, the methylation score of such sites is not informative, but may add noise to the deconvolution algorithm.

For each tissue 5000 CpG sites were selected that had the highest methylation score in that tissue compared with all other tissues, and the 5000 CpG sites that were most hypomethylated in that tissue were also selected (see Materials and Methods). From those possible 10,000 the 100 most hyper-methylated and the 100 most hypomethylated for each tissue (Table 1) were selected for deconvolution analysis. To these 200 sites from each tissue, any CpG within 150 base-pairs upstream or downstream of a selected CpG was added (Table 2). As this was done on a per CpG bases the number of sites in each tissue varied slightly. Lastly, 500 additional CpGs (Table 3) were added. These CpGs were iteratively selected as the most differentially methylated CpG site between the most similar pair of cell types, at each iteration.

Notably, other feature selection approaches are also possible; for example, it has been reported that others have relied mostly on a subset of methylation sites that are most variable among tissues; however, it was found that tissue-specific sites are superior to tissue-variable sites in deconvolution performance (FIG. 2B). Specificity values were estimated as the mean error (RMSE) for simulations with 0% mixing (namely, pure leukocytes), for convolution with different numbers of selected CpGs per cell types. These include the top K differentially hypermethylated CpGs and top K hypomethylated CpGs per cell types, with K varying from 50 CpGs (50×2×29=2900 total), through 100, 200, 500, 1000, 5000, or all methylation array CpGs (FIG. 2B, right-most column). Intriguingly, deconvolution with all CpGs is less accurate (and less efficient) compared to models with fewer, selected, CpGs. Sensitivity values, were estimated as percent of simulated mixes correctly detected at 0.1% and 1% mixing in. The optimal selection was found to be 500 hyper+500 hypomethylated CpGs per cell type, for total of 29,000 CpGs (FIG. 2B, orange bars). Deconvolution was also performed with the inclusion of all neighboring CpGs (up to 150 bp away) from previously selected ones. The addition of neighboring CpGs allows for accurate deconvolution with fewer CpGs, e.g. 2×100 CpG blocks per cell type (total of 8048 CpGs in 3,860 CpG “haplotype blocks”) (FIG. 2B, middle row). The inclusion of 500 CpGs with additional pairwise-specificity that are specifically selected to distinguish between similar cell types (e.g. different T cells, uterus vs. cervix, etc.), allowed for a further improvement in sensitivity and specificity with fewer CpGs (FIG. 2B, bottom row).

The Tissues/Cell Types are listed as follows: 1=Monocytes, 2=B-cells, 3=CD4+ T-cells, 4=NK-cells, 5=CD8+ T-cells, 6=Eosinophils, 7=Neutrophils, 8=Erythrocyte Progenitors, 9=Adipocytes, 10=Neurons, 11=Hepatocytes, 12=Lung alveolar cells, 13=Pancreatic Beta cells, 14=Pancreatic acinar cells, 15=Pancreatic duct cells, 16=vascular endothelial cells, 17=Left atrium, 18=bladder, 19=Breast, 20=Cervix, 21=Colon, 22=Esophagus, 23=Oral cavity, 24=Kidney, 25=Prostate, 26=Rectum, 27=Stomach, 28=Thyroid, 29=Uterus, 30=Lung bronchial cells, 31=Cholangiocytes, 32=Muscle, 33=Oligodendrocytes, and 34=Ovary.

MU=Most uniquely hyper/hypo-methylated, N=Neighboring

TABLE 1 Top 100 hyper-methylated sites per tissue/cell type Illumina Tissue/Cell Probe Name Chr. Position Type MU/N cg04221877 chr1 25257515 1 N cg15014975 chr1 25257547 1 MU cg24019564 chr1 25257566 1 N cg10993442 chr1 25257587 1 N cg24842859 chr1 25257599 1 N cg20695936 chr1 25257624 1 N cg13106389 chr1 25257626 1 N cg18087266 chr1 25257629 1 N cg16306706 chr1 42643274 1 MU cg07865091 chr1 43814306 1 N cg18856478 chr1 43814358 1 MU cg06723492 chr1 161135296 1 MU cg22109530 chr1 178310534 1 MU cg14515791 chr1 178310545 1 N cg03254916 chr1 201978949 1 MU cg21160472 chr1 212782112 1 MU cg24686918 chr1 221613658 1 MU cg13677741 chr1 236031096 1 MU cg05396897 chr1 247611448 1 N cg09226051 chr1 247611502 1 MU cg27388962 chr10 4705434 1 MU cg18675610 chr10 32216311 1 MU cg22706007 chr10 32216359 1 N cg14034325 chr10 43891459 1 MU cg14789659 chr10 63809073 1 MU cg00928816 chr10 63809098 1 N cg20746552 chr10 63809108 1 N cg16389209 chr10 63809121 1 N cg07520810 chr10 63809149 1 N cg16401465 chr10 63809170 1 N cg25103895 chr10 64565642 1 N cg20684110 chr10 64565750 1 MU cg01005506 chr10 64565768 1 N cg12474798 chr10 64565772 1 MU cg26033520 chr10 74004071 1 MU cg04858631 chr10 74035570 1 MU cg23231163 chr10 75533350 1 N cg19442545 chr10 75533431 1 MU cg16988611 chr10 82224946 1 MU cg15149596 chr11 19138954 1 MU cg07438103 chr11 47175143 1 MU cg03711944 chr11 47377212 1 MU cg16127573 chr11 57532299 1 MU cg10879244 chr11 64577190 1 N cg22897141 chr11 64577338 1 MU cg25170017 chr11 64644487 1 MU cg06889535 chr11 126455712 1 MU cg17572255 chr12 50676705 1 MU cg20260663 chr12 56211576 1 MU cg08425810 chr12 58132558 1 MU cg08832851 chr12 92535822 1 MU cg08857351 chr12 117627450 1 MU cg21737773 chr12 117627464 1 N cg02486646 chr12 117627478 1 N cg23247274 chr13 21750649 1 MU cg06340704 chr13 97877417 1 MU cg19300307 chr13 99136580 1 MU cg01424562 chr14 69256677 1 MU cg12827637 chr14 69256791 1 MU cg08169020 chr14 69256888 1 MU cg19692929 chr14 92575120 1 MU cg16062483 chr14 98444417 1 N cg16278496 chr14 98444476 1 MU cg11798182 chr14 98444513 1 N cg00034769 chr14 98444533 1 N cg25913761 chr15 90727560 1 MU cg26955540 chr15 90727570 1 MU cg06008511 chr15 90727886 1 N cg18569141 chr15 90727995 1 N cg16439823 chr15 90728020 1 N cg19331221 chr15 90728027 1 MU cg09465703 chr16 733842 1 MU cg05780543 chr16 46963764 1 MU cg09267773 chr16 79802467 1 MU cg09642825 chr16 87416230 1 MU cg03646327 chr16 88509768 1 N cg05507654 chr16 88509857 1 MU cg27189395 chr16 89725071 1 MU cg04043919 chr16 89725091 1 N cg21692159 chr17 36898937 1 MU cg10361922 chr17 40925790 1 MU cg02962602 chr17 80171413 1 MU cg02413187 chr19 1356269 1 N cg23674169 chr19 1356278 1 N cg13646005 chr19 1356304 1 N cg23057220 chr19 1356315 1 MU cg26282762 chr19 1356444 1 N cg08846870 chr19 15568360 1 MU cg05258935 chr19 39086923 1 MU cg22006825 chr19 41771771 1 N cg12946660 chr19 41771873 1 MU cg01771876 chr2 11886613 1 MU cg11845111 chr2 191398756 1 N cg27211696 chr2 191398769 1 N cg10560079 chr2 191398806 1 MU cg00982136 chr2 201726139 1 MU cg08474651 chr2 201729683 1 MU cg26047334 chr2 218785909 1 MU cg08063160 chr2 238578376 1 MU cg03504039 chr2 238578435 1 N cg22734058 chr2 238578491 1 N cg20074395 chr20 1246715 1 MU cg04053242 chr20 1246772 1 N cg08257293 chr20 30308440 1 MU cg01377358 chr20 30948211 1 MU cg05056497 chr21 35899448 1 MU cg14785464 chr21 38362725 1 MU cg06167719 chr21 38362727 1 MU cg03419014 chr21 38362742 1 N cg09037712 chr21 38362754 1 N cg25703541 chr22 24373054 1 N cg02953382 chr22 24373134 1 MU cg26460530 chr22 39919067 1 MU cg15376996 chr22 50912555 1 MU cg25381017 chr3 3151795 1 MU cg08404225 chr3 3151899 1 N cg13512830 chr3 149530294 1 N cg12461469 chr3 149530341 1 MU cg06398251 chr3 184033620 1 MU cg14855367 chr3 191048308 1 MU cg27312961 chr3 191048361 1 N cg13506158 chr3 191048377 1 N cg27269488 chr3 191048411 1 N cg00591949 chr3 191048439 1 N cg22879098 chr5 672845 1 MU cg24082121 chr5 672872 1 MU cg11895835 chr5 111078476 1 MU cg23256675 chr5 111078579 1 N cg17533455 chr5 130599827 1 MU cg22017733 chr5 162936453 1 MU cg23012579 chr5 172359545 1 MU cg18034299 chr5 172359680 1 N cg16416715 chr6 32942358 1 MU cg26264318 chr6 32942400 1 N cg25158622 chr6 32942429 1 N cg26953232 chr6 32942494 1 MU cg24275356 chr6 32942541 1 MU cg27395200 chr6 32942710 1 MU cg07275218 chr6 32942714 1 N cg17451586 chr6 32942808 1 N cg03780271 chr6 33280052 1 N cg12589538 chr6 33280149 1 N cg11796996 chr6 33280159 1 MU cg02863594 chr6 33280199 1 MU cg01253676 chr6 33280212 1 N cg20998791 chr6 33280228 1 N cg18353226 chr6 33280390 1 N cg14419102 chr6 33280400 1 N cg06375761 chr6 33280408 1 MU cg26083458 chr6 33280424 1 N cg13638257 chr6 33280436 1 MU cg06493154 chr6 42859023 1 MU cg20227056 chr6 130096463 1 MU cg25399239 chr6 139013092 1 N cg18136963 chr6 139013146 1 MU cg23422583 chr7 6388872 1 MU cg11532431 chr7 27169674 1 MU cg00562553 chr7 27169740 1 N cg04317399 chr7 27170313 1 N cg07317062 chr7 27170388 1 N cg19142026 chr7 27170394 1 MU cg11410718 chr7 27170412 1 N cg23220823 chr7 45041140 1 MU cg05875700 chr8 638208 1 MU cg12641240 chr8 638330 1 N cg00936790 chr8 29027844 1 MU cg04962480 chr8 101962118 1 MU cg23006204 chr9 130700866 1 N cg14395885 chr9 130700923 1 N cg21181453 chr9 130700954 1 MU cg00921973 chr9 130701076 1 N cg07558455 chr1 62784762 2 N cg16028201 chr1 62784802 2 MU cg13673164 chr1 110009509 2 MU cg04997273 chr1 117452884 2 MU cg11221228 chr1 117452905 2 N cg24862510 chr1 164290545 2 MU cg18222582 chr1 232765331 2 N cg16026114 chr1 232765417 2 MU cg11158057 chr1 239549798 2 MU cg08542090 chr1 239549830 2 N cg23684246 chr1 239549853 2 N cg22017581 chr1 239550177 2 N cg13985485 chr1 239550283 2 N cg13570831 chr1 239550303 2 MU cg17426273 chr10 21462441 2 MU cg08564661 chr10 124133785 2 MU cg03205140 chr10 124133802 2 N cg10576280 chr10 124133822 2 N cg05858607 chr10 128593624 2 N cg00622654 chr10 128593718 2 MU cg06128198 chr10 128593922 2 MU cg22665692 chr10 128593943 2 MU cg23665778 chr10 128593946 2 N cg18932726 chr10 128594141 2 N cg05608777 chr10 128594144 2 MU cg02300377 chr11 12399056 2 N cg26320696 chr11 12399109 2 N cg18778433 chr11 12399176 2 MU cg18270629 chr11 31831974 2 MU cg16802508 chr11 67070738 2 N cg25915131 chr11 67070756 2 N cg19176391 chr11 67070760 2 MU cg05289892 chr11 67070816 2 MU cg25753024 chr11 67070913 2 MU cg08662757 chr11 67070967 2 MU cg26991761 chr11 67071064 2 N cg03565189 chr11 69924724 2 MU cg02788102 chr11 115374969 2 N cg19697530 chr11 115375071 2 MU cg14944514 chr11 115375192 2 N cg02083559 chr12 24103358 2 MU cg27523779 chr12 24103374 2 N cg22249612 chr12 56121485 2 MU cg24642065 chr12 130527030 2 N cg13576883 chr12 130527038 2 N cg15584445 chr12 130527046 2 N cg10233825 chr12 130527104 2 MU cg02681173 chr12 130527106 2 N cg25591794 chr13 28554842 2 MU cg06621094 chr13 75149963 2 MU cg18656829 chr13 100632250 2 MU cg20327444 chr13 110959647 2 MU cg17541528 chr13 110959650 2 N cg01956420 chr13 110959668 2 N cg18641486 chr14 37641660 2 N cg23291305 chr14 37641726 2 MU cg18909638 chr14 37666810 2 N cg01841758 chr14 37666893 2 N cg06802658 chr14 37666920 2 N cg18655441 chr14 37666931 2 MU cg10644361 chr14 37667230 2 N cg23919479 chr14 37667261 2 MU cg04925858 chr14 77499547 2 MU cg02971654 chr14 79745343 2 N cg07231787 chr14 79745403 2 N cg21918126 chr14 79745471 2 MU cg01520867 chr14 104583632 2 MU cg06218726 chr14 104583740 2 N cg27021357 chr15 84115969 2 N cg22946150 chr15 84116107 2 N cg21145136 chr15 84116115 2 MU cg09201151 chr15 84116151 2 N cg21318213 chr15 96875591 2 N cg04330371 chr15 96875656 2 N cg06926934 chr15 96875675 2 MU cg10040836 chr15 96875689 2 N cg00367497 chr15 96875716 2 N cg23042706 chr15 96875781 2 N cg03437748 chr15 99193247 2 MU cg02056682 chr16 54965084 2 MU cg05870072 chr16 54965087 2 N cg05362516 chr16 54965089 2 N cg06191898 chr16 54965100 2 N cg09641689 chr16 54965142 2 N cg08974450 chr16 84402160 2 MU cg10282162 chr17 11144311 2 MU cg23953396 chr17 72427941 2 MU cg07910813 chr18 65183745 2 MU cg12975230 chr18 73167671 2 MU cg24218925 chr19 2578938 2 N cg13650938 chr19 2579075 2 MU cg22746789 chr19 2579221 2 N cg15208375 chr19 31841555 2 N cg11199770 chr19 31841663 2 MU cg03287340 chr19 50651195 2 MU cg19145730 chr2 46043 2 MU cg14255256 chr2 46465 2 MU cg01880096 chr2 46558 2 N cg13021841 chr2 14774902 2 N cg05602862 chr2 14775046 2 MU cg11533098 chr2 14775132 2 N cg03858756 chr2 176994637 2 N cg15808943 chr2 176994665 2 N cg24416513 chr2 176994764 2 MU cg11041835 chr2 189156425 2 MU cg17931661 chr2 189156549 2 N cg13895867 chr2 237076164 2 N cg19052355 chr2 237076306 2 MU cg22654039 chr20 11871282 2 N cg07436991 chr20 11871311 2 N cg14603345 chr20 11871375 2 N cg23944804 chr20 11871384 2 MU cg12894649 chr20 11871396 2 N cg04624110 chr20 13976093 2 N cg01552272 chr20 13976096 2 N cg23169957 chr20 13976106 2 N cg25557432 chr20 13976117 2 MU cg04716990 chr20 13976126 2 MU cg06571075 chr20 13976143 2 N cg26059153 chr20 13976190 2 MU cg23687000 chr21 38071673 2 MU cg14769207 chr21 38071807 2 N cg16155122 chr22 25201958 2 N cg09097345 chr22 25202086 2 MU cg14179908 chr22 44319666 2 N cg13184872 chr22 44319719 2 MU cg24333845 chr3 152016806 2 MU cg22079161 chr4 7940579 2 MU cg04859706 chr4 30721997 2 MU cg13624385 chr4 30722004 2 N cg25894334 chr4 77172836 2 MU cg08073591 chr4 77172841 2 MU cg17559786 chr4 77172861 2 N cg21599230 chr4 118006619 2 MU cg24183909 chr4 118006747 2 N cg07837556 chr4 118006750 2 N cg23001131 chr4 152246547 2 MU cg24634420 chr4 157892748 2 MU cg20937139 chr4 157892758 2 N cg22451233 chr4 157892765 2 N cg08712866 chr4 157997526 2 MU cg11163620 chr4 157997554 2 N cg25107254 chr4 187476455 2 N cg08915603 chr4 187476569 2 MU cg09473510 chr4 187476573 2 MU cg06293787 chr4 187476608 2 MU cg12363722 chr4 187476703 2 N cg16248329 chr4 187644739 2 MU cg13658542 chr4 187645059 2 MU cg01244944 chr4 187645118 2 N cg27475132 chr4 187645120 2 MU cg24410535 chr4 187645144 2 N cg24675098 chr5 35617879 2 N cg27003682 chr5 35617945 2 MU cg01581237 chr5 35617969 2 N cg00579717 chr5 170289638 2 MU cg17156565 chr5 174151492 2 N cg25998402 chr5 174151498 2 MU cg12387713 chr5 174151567 2 N cg15123984 chr5 174151634 2 N cg24996482 chr5 174178256 2 MU cg01096398 chr6 1312475 2 MU cg03730958 chr6 1312519 2 N cg27336026 chr6 1312532 2 N cg05685587 chr6 1312577 2 N cg26422161 chr6 1313211 2 MU cg24636336 chr6 19805167 2 MU cg06526693 chr6 19805277 2 N cg17833071 chr6 33084770 2 N cg23075555 chr6 33084789 2 N cg13524302 chr6 33084798 2 MU cg02662362 chr6 33084814 2 MU cg24465429 chr6 33084825 2 MU cg24266485 chr6 33084840 2 N cg24185832 chr6 33084888 2 N cg05337177 chr6 33084928 2 N cg23640002 chr6 33084933 2 N cg22466350 chr6 128841137 2 MU cg17364114 chr6 160769359 2 MU cg07237939 chr6 160769754 2 MU cg16053716 chr7 193438 2 N cg07615067 chr7 193580 2 MU cg19815904 chr7 80548307 2 N cg03823904 chr7 80548456 2 MU cg12456681 chr7 89748633 2 N cg24617568 chr7 89748647 2 N cg05884394 chr7 89748671 2 N cg03098837 chr7 89748686 2 MU cg04148584 chr7 107643946 2 MU cg26087411 chr7 107643948 2 MU cg17657268 chr7 107643956 2 MU cg09284719 chr7 107644024 2 N cg19101326 chr8 106331160 2 MU cg17154315 chr8 106331166 2 N cg11585820 chr8 145925948 2 N cg09979478 chr8 145925997 2 MU cg03134157 chr8 145926089 2 N cg06671450 chr9 36985987 2 N cg14317609 chr9 36986006 2 MU cg14344261 chr9 36986380 2 MU cg13910860 chr9 36986407 2 MU cg13685727 chr9 90589530 2 MU cg16357921 chr9 90589634 2 N cg13920435 chr9 90589640 2 MU cg24163210 chr1 1713944 3 N cg00558749 chr1 1713950 3 N cg13461447 chr1 1714001 3 MU cg10207218 chr1 1714140 3 N cg19276111 chr1 24229232 3 MU cg24452347 chr1 24229300 3 MU cg26404511 chr1 24229575 3 MU cg27424995 chr1 27902555 3 MU cg27543538 chr1 27902687 3 N cg06086731 chr1 38942075 3 N cg02983950 chr1 38942220 3 MU cg04406436 chr1 38942313 3 N cg05388840 chr1 41981911 3 N cg12034678 chr1 41982027 3 MU cg11177980 chr1 41982115 3 N cg08894594 chr1 62208631 3 MU cg10316635 chr1 62208645 3 MU cg21279955 chr1 153747551 3 N cg19773045 chr1 153747600 3 N cg06239526 chr1 153747609 3 N cg24408896 chr1 153747658 3 N cg16247817 chr1 153747686 3 MU cg08623548 chr1 153936729 3 MU cg26008365 chr1 153958797 3 MU cg06121450 chr1 165798382 3 N cg15473346 chr1 165798420 3 MU cg21161394 chr1 167408709 3 N cg02648847 chr1 167408735 3 N cg12446199 chr1 167408841 3 MU cg01565130 chr1 167690726 3 MU cg06912814 chr1 201139740 3 N cg27557428 chr1 201139878 3 MU cg10040748 chr1 211431567 3 MU cg07363543 chr1 224691275 3 MU cg05396897 chr1 247611448 3 N cg09226051 chr1 247611502 3 MU cg03680517 chr10 43632967 3 N cg07178550 chr10 43633030 3 MU cg08388111 chr10 74032820 3 MU cg21344746 chr10 80831230 3 MU cg13954667 chr10 88295472 3 N cg13331196 chr10 88295591 3 MU cg08496387 chr11 11862730 3 N cg12492653 chr11 11862867 3 N cg02501827 chr11 11862879 3 MU cg15846482 chr11 18610557 3 MU cg21272996 chr11 57529255 3 MU cg18213545 chr11 57529400 3 N cg10831212 chr11 57529419 3 MU cg19210276 chr11 57529465 3 MU cg09878888 chr11 57529614 3 N cg15995296 chr11 67210812 3 MU cg26858704 chr12 4918848 3 MU cg24621362 chr12 6492890 3 N cg19476647 chr12 6492948 3 N cg23079808 chr12 6493003 3 MU cg01607849 chr12 6976129 3 MU cg05795313 chr12 48745136 3 MU cg04545296 chr12 48745243 3 N cg10183001 chr12 51318430 3 N cg13437525 chr12 51318487 3 MU cg22249612 chr12 56121485 3 MU cg02003159 chr12 107711614 3 N cg24124443 chr12 107711648 3 MU cg10421247 chr12 120524653 3 MU cg26406891 chr12 125002940 3 N cg15085899 chr12 125003064 3 MU cg25365958 chr13 42615991 3 MU cg26685941 chr13 95952902 3 MU cg05412028 chr13 95952937 3 MU cg03103888 chr13 114147763 3 MU cg04542489 chr14 23775746 3 N cg25149155 chr14 23775777 3 N cg12917475 chr14 23775787 3 N cg05877109 chr14 23775794 3 MU cg18976418 chr14 39735496 3 MU cg14873488 chr14 39735625 3 MU cg23051349 chr14 104094779 3 MU cg05321721 chr14 104094918 3 N cg20900050 chr15 31689509 3 N cg12426092 chr15 31689570 3 MU cg20629587 chr15 31689706 3 N cg02365220 chr15 50474069 3 N cg25417405 chr15 50474129 3 N cg25150243 chr15 50474159 3 MU cg25729350 chr15 58624534 3 MU cg23600866 chr15 99791109 3 MU cg08640252 chr16 1820949 3 MU cg06074992 chr16 71598553 3 MU cg06126721 chr17 1478065 3 MU cg23837623 chr17 1478208 3 N cg03666441 chr17 7254671 3 MU cg05164926 chr17 7255624 3 MU cg10479431 chr17 7461421 3 N cg05514680 chr17 7461556 3 MU cg23161218 chr17 7461638 3 N cg26695387 chr17 21189768 3 N cg15416179 chr17 21189859 3 MU cg22935450 chr17 38465393 3 N cg03053374 chr17 38465422 3 N cg00236832 chr17 38465489 3 N cg26654286 chr17 38465510 3 MU cg10409248 chr17 77775073 3 MU cg04173586 chr19 2167496 3 MU cg27073066 chr19 2169160 3 MU cg17100943 chr19 6459300 3 MU cg13621184 chr19 7459697 3 MU cg25737313 chr19 12899557 3 MU cg21869046 chr19 41225005 3 MU cg25784219 chr2 28618328 3 MU cg04011671 chr2 43202474 3 MU cg03717364 chr2 43202481 3 MU cg00168785 chr2 160142643 3 MU cg20862669 chr2 160142734 3 N cg03997390 chr2 201170790 3 N cg18609578 chr2 201170806 3 MU cg15650509 chr2 237476609 3 MU cg14897238 chr21 43198283 3 N cg21863949 chr21 43198431 3 MU cg27653384 chr22 22293118 3 MU cg25703541 chr22 24373054 3 N cg02953382 chr22 24373134 3 MU cg23595222 chr22 46449430 3 N cg26394940 chr22 46449461 3 N cg11153400 chr22 46449498 3 MU cg01080924 chr22 46449550 3 N cg01564818 chr3 9464436 3 MU cg01870865 chr3 48507087 3 MU cg14870461 chr3 69062103 3 MU cg03399783 chr3 69062163 3 N cg14612335 chr3 170074131 3 MU cg13607248 chr3 183874556 3 MU cg10683341 chr4 39639289 3 MU cg23651614 chr4 39639327 3 N cg19933595 chr4 146857013 3 MU cg01894064 chr4 154073371 3 N cg01022974 chr4 154073480 3 MU cg00349895 chr4 154073501 3 MU cg00381131 chr4 154073616 3 N cg15784646 chr4 159707693 3 MU cg19493077 chr5 32613266 3 MU cg19725489 chr5 72252795 3 MU cg24531022 chr5 102201825 3 MU cg10640944 chr5 102201890 3 N cg11006267 chr5 139017424 3 MU cg25606201 chr5 180614858 3 MU cg25728685 chr6 28351148 3 MU cg19930737 chr6 44044325 3 MU cg16088539 chr6 44044443 3 N cg20865323 chr6 143999666 3 MU cg22707529 chr6 143999715 3 MU cg15232539 chr8 19459672 3 MU 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27902687 5 N cg02447380 chr1 28919300 5 MU cg02525756 chr1 28919362 5 N cg24118052 chr1 28919387 5 N cg05388840 chr1 41981911 5 N cg12034678 chr1 41982027 5 MU cg11177980 chr1 41982115 5 N cg24312719 chr1 93251184 5 MU cg17140307 chr1 93251203 5 N cg11226183 chr1 93251218 5 N cg08978953 chr1 120190520 5 N cg09266149 chr1 120190550 5 MU cg27431596 chr1 120190588 5 N cg18086761 chr1 151763997 5 MU cg20686828 chr1 151764147 5 N cg11411904 chr1 153935719 5 MU cg08623548 chr1 153936729 5 MU cg06121450 chr1 165798382 5 N cg15473346 chr1 165798420 5 MU cg21161394 chr1 167408709 5 N cg02648847 chr1 167408735 5 N cg12446199 chr1 167408841 5 MU cg00909514 chr1 200860534 5 MU cg08360820 chr10 11652640 5 MU cg22620363 chr10 80826794 5 MU cg21344746 chr10 80831230 5 MU cg25011475 chr10 88517608 5 MU cg22074576 chr11 3181726 5 MU cg11678461 chr11 3181822 5 N cg23357130 chr11 3181928 5 MU cg04717837 chr11 3225004 5 N cg03347559 chr11 3225059 5 MU cg11496778 chr11 3225076 5 N cg18213545 chr11 57529400 5 N cg10831212 chr11 57529419 5 MU cg19210276 chr11 57529465 5 N cg03101664 chr11 64794610 5 N cg24585346 chr11 64794741 5 N cg19146924 chr11 64794755 5 MU cg06065225 chr11 65640137 5 MU cg08092930 chr11 70117714 5 MU cg04983516 chr11 79151719 5 MU cg26426447 chr11 118134959 5 N cg09033131 chr11 118135094 5 MU cg16594165 chr11 118135105 5 N cg07315693 chr11 118135203 5 N cg26202340 chr11 126152357 5 N cg02305850 chr11 126152462 5 N cg02973469 chr11 126152481 5 MU cg03963011 chr11 128418551 5 N cg25244476 chr11 128418692 5 MU cg20645074 chr12 49182479 5 MU cg24235581 chr12 58290452 5 MU cg06003296 chr12 71003681 5 MU cg02003159 chr12 107711614 5 N cg24124443 chr12 107711648 5 MU cg08273772 chr12 112035212 5 N cg06923988 chr12 112035285 5 MU cg22529546 chr12 112819040 5 N cg11287899 chr12 112819167 5 MU cg15023427 chr12 112819249 5 N cg10421247 chr12 120524653 5 MU cg26406891 chr12 125002940 5 N cg15085899 chr12 125003064 5 MU cg27485716 chr12 133286734 5 MU cg25365958 chr13 42615991 5 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21852325 5 MU cg26398656 chr18 21852327 5 N cg06593258 chr18 21852333 5 N cg21840153 chr18 21852436 5 N cg08894153 chr19 12606729 5 MU cg05022378 chr19 12606767 5 N cg25737313 chr19 12899557 5 MU cg03287340 chr19 50651195 5 MU cg24152605 chr19 57050359 5 N cg22054362 chr19 57050367 5 MU cg21970554 chr19 57050370 5 N cg04011671 chr2 43202474 5 MU cg03717364 chr2 43202481 5 N cg19286686 chr2 158731699 5 N cg23967461 chr2 158731736 5 MU cg01943873 chr2 158731796 5 N cg10903451 chr2 164592851 5 MU cg00490655 chr2 164592854 5 N cg15650509 chr2 237476609 5 MU cg22654039 chr20 11871282 5 N cg07436991 chr20 11871311 5 N cg14603345 chr20 11871375 5 N cg23944804 chr20 11871384 5 MU cg12894649 chr20 11871396 5 N cg10546252 chr22 24384294 5 N cg24238852 chr22 24384340 5 N cg11478607 chr22 24384400 5 MU cg09805010 chr3 24536077 5 MU cg02333852 chr3 24536252 5 MU cg20985755 chr3 24536474 5 MU cg13790603 chr3 24536478 5 N cg22306344 chr3 49966773 5 MU cg23320649 chr3 50604613 5 N cg10779423 chr3 50604763 5 MU cg02321062 chr3 107244746 5 MU cg01655150 chr3 126195255 5 N cg16898124 chr3 126195383 5 MU cg12349858 chr3 160822545 5 MU cg14612335 chr3 170074131 5 MU cg13607248 chr3 183874556 5 MU cg11836119 chr4 206874 5 MU cg22913249 chr4 41215979 5 MU cg18587806 chr4 41362843 5 MU cg26248284 chr4 41362853 5 N cg17187287 chr4 115519593 5 MU cg01803059 chr4 146402404 5 N cg02971575 chr4 146402540 5 MU cg18485193 chr5 16936369 5 MU cg17942573 chr5 16936391 5 N cg07705810 chr5 16936470 5 N cg19493077 chr5 32613266 5 MU cg19725489 chr5 72252795 5 MU cg13904970 chr5 123987667 5 MU cg25606201 chr5 180614858 5 MU cg25728685 chr6 28351148 5 MU cg23197007 chr6 30655617 5 N cg13357602 chr6 30655720 5 MU cg09424348 chr6 30881549 5 N cg15080939 chr6 30881560 5 N cg05267955 chr6 30881562 5 N cg18261909 chr6 30881571 5 N cg27650171 chr6 30881579 5 N cg16555181 chr6 30881643 5 N cg02186769 chr6 30881645 5 N cg26860970 chr6 30881658 5 N cg05103231 chr6 30881664 5 MU cg00649377 chr6 30881728 5 N 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44326516 9 N cg14045803 chr11 72492461 9 N cg08749455 chr11 72492538 9 MU cg21303763 chr12 3309708 9 N cg23065927 chr12 3309816 9 MU cg25021051 chr12 53448178 9 MU cg09494609 chr12 53448180 9 MU cg15628956 chr12 53448234 9 MU cg18922524 chr12 54447136 9 N cg03923561 chr12 54447220 9 MU cg02264990 chr12 54447243 9 MU cg09720701 chr12 54447283 9 MU cg01579535 chr12 54447292 9 N cg01524853 chr12 54447807 9 N cg22747076 chr12 54447873 9 MU cg18563999 chr12 54785003 9 N cg03946762 chr12 54785043 9 N cg20967139 chr12 54785055 9 MU cg18886436 chr12 54785171 9 MU cg07000334 chr12 54785286 9 N cg11856711 chr12 54785309 9 N cg17655624 chr12 54785320 9 N cg08900101 chr12 114833043 9 MU cg05065572 chr12 114833912 9 N cg12610207 chr12 114833950 9 MU cg01519253 chr12 114886463 9 MU cg15964309 chr12 114886561 9 N cg24722950 chr14 62035475 9 N cg21890551 chr14 62035585 9 MU cg04961582 chr14 95239464 9 N cg19870698 chr14 95239570 9 MU cg01796166 chr14 95239586 9 N cg13660279 chr16 54318221 9 N 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cg04348265 chr4 186733060 9 N cg01054755 chr5 138728685 9 MU cg24761867 chr5 138728704 9 MU cg01278596 chr5 139228062 9 MU cg15992535 chr5 139228150 9 MU cg02009088 chr5 139228153 9 MU cg01627750 chr5 139228242 9 N cg00050152 chr5 180479829 9 MU cg02337825 chr5 180480982 9 MU cg26997880 chr6 32055370 9 N cg11823230 chr6 32055385 9 N cg05483184 chr6 32055393 9 N cg21460606 chr6 32055402 9 MU cg19807376 chr6 32055447 9 N cg15697476 chr6 32055474 9 N cg09996288 chr6 32055517 9 MU cg17001689 chr6 32055525 9 N cg02740950 chr6 32055534 9 N cg18178010 chr6 32055629 9 N cg00704305 chr6 127664950 9 MU cg01425666 chr7 5468436 9 MU cg06550177 chr7 5469535 9 MU cg01145709 chr7 10979247 9 MU cg08929390 chr7 10979341 9 N cg26551975 chr7 27211555 9 MU cg24988255 chr7 27225396 9 N cg25901381 chr7 27225485 9 N cg17466857 chr7 27225528 9 MU cg10657141 chr7 27225543 9 N cg02087954 chr7 100609534 9 MU cg03185704 chr7 150020125 9 N cg18221429 chr7 150020136 9 N cg07753583 chr7 150020206 9 N cg22747650 chr7 150020218 9 N cg11026333 chr7 150020269 9 MU cg01270001 chr7 150020401 9 MU cg22893248 chr7 150020751 9 N cg21516287 chr7 150020855 9 N cg02639123 chr7 150020881 9 MU cg02119840 chr7 150020900 9 N cg11347411 chr7 150020925 9 N cg11740103 chr7 150020945 9 N cg04624228 chr8 38411598 9 N cg04458368 chr8 38411682 9 N cg24859937 chr8 38411708 9 MU cg15183991 chr8 145047566 9 N cg00886954 chr8 145047713 9 MU cg08455275 chr8 145047979 9 MU cg11224624 chr8 145049280 9 MU cg08909802 chr8 145049419 9 N cg21183329 chr9 124976219 9 MU cg14061378 chr9 139539092 9 MU cg01868612 chr1 2478046 10 N cg03529908 chr1 2478089 10 MU cg02299950 chr1 3087637 10 N cg20154143 chr1 3087709 10 N cg14217558 chr1 3087734 10 MU cg05347736 chr1 6302164 10 MU cg22733648 chr1 6302273 10 N cg15524063 chr1 43832916 10 MU cg25385412 chr1 43832951 10 MU cg03793131 chr1 43833547 10 N cg22397520 chr1 43833587 10 N cg24729481 chr1 43833600 10 N cg24838029 chr1 43833615 10 MU cg08936794 chr1 43833873 10 N cg20301722 chr1 43833891 10 MU cg18540614 chr1 43833945 10 N cg11798658 chr1 47082039 10 MU cg23589045 chr1 51983759 10 MU cg07976644 chr1 55266932 10 MU cg19808825 chr1 55267006 10 N cg22130834 chr1 55267046 10 N cg01240734 chr10 71892593 10 MU cg15779600 chr10 71892694 10 N cg26699283 chr10 71892715 10 N cg20241658 chr10 71892719 10 N cg18993457 chr11 441983 10 N cg04021962 chr11 441986 10 MU cg05904366 chr11 442072 10 N cg24932241 chr11 442077 10 N cg24064264 chr11 615945 10 N cg07652350 chr11 616009 10 N cg13145591 chr11 616038 10 MU cg13378284 chr11 616089 10 N cg18971819 chr11 616112 10 N cg05122965 chr11 5706362 10 MU cg01799588 chr11 5706381 10 N cg12471950 chr11 5706401 10 N cg24098643 chr11 5706435 10 N cg09347923 chr11 47736425 10 N cg19112957 chr11 47736463 10 MU cg25168700 chr11 64067685 10 N cg24027931 chr11 64067687 10 N cg04855748 chr11 64067763 10 N cg18383813 chr11 64067793 10 N cg09675196 chr11 64067795 10 MU cg05098417 chr11 64067858 10 N cg23069847 chr11 64067868 10 N cg15217978 chr11 65343330 10 N cg02532488 chr11 65343341 10 N cg18765439 chr11 65343343 10 MU cg10725932 chr11 71934276 10 MU cg22884792 chr12 49371987 10 MU cg09158990 chr12 49372112 10 N cg02067983 chr12 49372131 10 N cg12693595 chr12 57504318 10 N cg00500522 chr12 57504387 10 MU cg22905097 chr13 43566401 10 N cg19915711 chr13 43566421 10 N cg22125968 chr13 43566472 10 MU cg01536987 chr13 43566492 10 MU cg03478249 chr13 43566547 10 N cg12138678 chr13 43566618 10 N cg16327891 chr13 43566633 10 N cg26714230 chr13 43566642 10 N cg18732064 chr14 23356059 10 MU cg03577460 chr14 24836084 10 N cg05330668 chr14 24836090 10 MU cg25076767 chr14 24836148 10 MU cg15346168 chr14 24836194 10 N cg26705574 chr14 24836219 10 N cg04134240 chr14 100680625 10 MU cg24498031 chr15 40545050 10 N cg13892980 chr15 40545143 10 MU cg18097281 chr15 40545145 10 MU cg12635019 chr15 40545175 10 MU cg04305879 chr15 40545215 10 N cg11939465 chr15 40545224 10 N cg05068428 chr15 40545296 10 MU cg01036164 chr15 40545324 10 N cg04812615 chr15 40545326 10 N cg05426702 chr15 40574544 10 MU cg22409276 chr15 40763763 10 MU cg24740818 chr15 76440405 10 N cg08557928 chr15 76440443 10 MU cg18021161 chr15 76440448 10 MU cg14912033 chr15 79043428 10 N cg07120314 chr15 79043507 10 MU cg03148231 chr15 89954845 10 MU cg10308906 chr15 89954991 10 N cg12258919 chr16 2521473 10 MU cg10504751 chr16 56390830 10 MU cg03022368 chr16 83986702 10 N cg08589960 chr16 83986755 10 MU cg03891598 chr16 83986782 10 N cg00187635 chr16 83986795 10 N cg04313875 chr17 1553202 10 MU cg15380836 chr17 1553341 10 N cg08967106 chr17 35014412 10 MU cg00371418 chr17 35014438 10 N cg08377924 chr17 41363628 10 N cg19548479 chr17 41363737 10 N cg24940138 chr17 41363741 10 MU cg21211480 chr17 41363774 10 MU cg15026277 chr17 41363789 10 MU cg03049782 chr17 41363891 10 N cg24008544 chr17 41363899 10 N cg18222083 chr17 41364007 10 MU cg04482110 chr17 41364121 10 N cg10842515 chr17 43325167 10 N cg19411214 chr17 43325316 10 MU cg25533943 chr17 43325323 10 N cg21185826 chr17 47979513 10 MU cg03877957 chr17 47979595 10 N cg00023001 chr17 55938748 10 N cg22451358 chr17 55938871 10 MU cg06452419 chr17 55938940 10 N cg14511665 chr17 62773096 10 MU cg06792847 chr17 62773246 10 N cg19598544 chr17 72209156 10 N cg14506834 chr17 72209198 10 MU cg11887884 chr17 79486500 10 MU cg18395558 chr19 12939446 10 N cg09463900 chr19 12939587 10 MU cg16005540 chr19 13983813 10 MU cg27346356 chr19 19648914 10 N cg23479742 chr19 19648939 10 N cg01487433 chr19 19648987 10 N cg21073520 chr19 19648991 10 N cg13674885 chr19 19649041 10 N cg09386096 chr19 19649054 10 MU cg10313673 chr19 19649144 10 N cg01854969 chr19 35644903 10 MU cg09966364 chr19 35644908 10 N cg11840540 chr19 35644996 10 N cg22618240 chr19 35645556 10 N cg26721700 chr19 35645598 10 N cg09386954 chr19 35645620 10 MU cg11260421 chr19 35645680 10 N cg16860694 chr19 35645689 10 MU cg10227705 chr19 35645712 10 MU cg03096975 chr19 46142574 10 N cg07804711 chr19 46142647 10 N cg12536502 chr19 46142673 10 MU cg07749923 chr19 46142711 10 N cg01544351 chr19 46142757 10 N cg23696132 chr19 46273771 10 MU cg14149007 chr19 47219555 10 MU cg23864632 chr19 47220401 10 MU cg12568362 chr19 47220607 10 N cg08230695 chr19 47220755 10 MU cg13685883 chr19 47220848 10 N cg09829983 chr2 7017698 10 N cg18201077 chr2 7017787 10 MU cg26360792 chr2 43019997 10 N cg27299406 chr2 43020013 10 MU cg09480054 chr2 43020129 10 N cg22194948 chr2 43447139 10 MU cg22198946 chr2 43454609 10 MU cg06907744 chr2 43454761 10 N cg16202615 chr2 43454763 10 N cg00338928 chr2 43454817 10 N cg23201549 chr2 43454852 10 MU cg03771385 chr2 43454876 10 N cg23038392 chr2 43454950 10 N cg04707074 chr2 43454972 10 N cg15300730 chr2 43454991 10 N cg09456094 chr2 231280878 10 N cg11827097 chr2 231280903 10 N cg11702456 chr2 231280907 10 MU cg26008007 chr2 232545866 10 MU cg09030830 chr20 43280135 10 N cg11091439 chr20 43280257 10 MU cg04839664 chr20 55964528 10 MU cg24210478 chr20 62733486 10 MU cg17297071 chr21 26934424 10 N cg23433889 chr21 26934455 10 MU cg12749863 chr21 26934576 10 N cg17506742 chr21 34638971 10 N cg01855674 chr21 34639040 10 N cg14280424 chr21 34639113 10 MU cg16007279 chr22 38598948 10 MU cg04169403 chr22 45098800 10 N cg01608788 chr22 45098907 10 MU cg18772127 chr3 32443496 10 MU cg02829783 chr3 38180432 10 N cg27043630 chr3 38180527 10 MU cg11816841 chr3 46734995 10 N cg05369142 chr3 46735009 10 MU cg19998675 chr3 182970903 10 N cg20585088 chr3 182970927 10 N cg12930573 chr3 182971009 10 N cg16938490 chr3 182971025 10 MU cg02238826 chr3 182971069 10 N cg11102611 chr3 182971663 10 MU cg12927498 chr5 141031122 10 MU cg09904383 chr5 141031125 10 N cg15844835 chr5 141031142 10 MU cg08742288 chr5 141031147 10 MU cg13267747 chr5 141031176 10 N cg01849970 chr5 141031209 10 N cg07844112 chr5 180230022 10 N cg19605920 chr5 180230113 10 MU cg16332813 chr5 180230115 10 N cg15177964 chr5 180230130 10 N cg07218275 chr5 180230146 10 N cg09867128 chr6 31795671 10 MU cg03731673 chr6 33393599 10 N cg04614173 chr6 33393607 10 MU cg06789667 chr6 33393635 10 N cg03650397 chr6 159239229 10 MU cg17222941 chr7 77648567 10 MU cg10271408 chr7 77648600 10 N cg18078841 chr7 77648624 10 N cg12025522 chr7 77649330 10 MU cg04976780 chr7 77649540 10 MU cg01914455 chr7 77649564 10 N cg03574119 chr7 100728591 10 N cg22607212 chr7 100728652 10 N cg06772157 chr7 100728711 10 N cg05401447 chr7 100728731 10 MU cg23063913 chr7 100728792 10 N cg26385074 chr7 100816740 10 MU cg15654626 chr8 103613080 10 MU cg11995594 chr8 103613219 10 N cg25548415 chr8 145016475 10 MU cg25678052 chr8 145016627 10 N cg19407252 chr8 145016746 10 MU cg22840464 chr8 145016828 10 N cg21550172 chr8 145018075 10 N cg26763284 chr8 145018185 10 MU cg10319399 chr8 145018285 10 N cg23715237 chr8 145018300 10 N cg14154487 chr9 125133314 10 MU cg12927153 chr9 125133318 10 MU cg15090202 chr1 26687087 11 MU cg04661629 chr1 26687174 11 N cg22224989 chr1 27986522 11 MU cg11224362 chr1 40041493 11 MU cg00630164 chr1 41250062 11 N cg13438961 chr1 41250071 11 MU cg26520930 chr1 41250117 11 N cg17952661 chr1 145395753 11 MU cg02395363 chr1 145395846 11 N cg09897416 chr1 149859252 11 MU cg04148762 chr1 203598573 11 N cg17721249 chr1 203598594 11 MU cg26253438 chr1 203598623 11 N cg21397588 chr1 203598954 11 N cg02457623 chr1 203599089 11 MU cg26271110 chr1 206730630 11 MU cg11037818 chr1 208041879 11 N cg16549711 chr1 208041887 11 MU cg18224445 chr1 223900693 11 MU cg07395969 chr1 223900822 11 N cg02899039 chr1 249152378 11 N cg11817309 chr1 249152443 11 MU cg00773413 chr10 77168431 11 MU cg25862644 chr10 77168553 11 N cg11700824 chr10 88730823 11 N cg12191938 chr10 88730946 11 MU cg19837790 chr10 97804137 11 MU cg06207432 chr10 97804280 11 N cg06154084 chr10 102792249 11 MU cg12137206 chr10 113943397 11 MU cg24677674 chr10 113943468 11 N cg11915650 chr11 2161009 11 N cg17434309 chr11 2161079 11 N cg05859777 chr11 2161102 11 N cg05452899 chr11 2161133 11 N cg10037494 chr11 2161135 11 N cg26517849 chr11 2161141 11 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100259329 11 MU cg14185025 chr14 100259341 11 N cg23411981 chr14 105940377 11 MU cg03451296 chr14 105940391 11 MU cg26437123 chr15 74726423 11 MU cg08125401 chr15 74726457 11 N cg06263834 chr15 74726469 11 N cg26095405 chr15 74726486 11 N cg13462576 chr15 74726520 11 N cg20927731 chr15 74726561 11 N cg26585724 chr15 90208739 11 N cg05009389 chr15 90208810 11 MU cg26703661 chr15 90208915 11 N cg02454890 chr15 90544015 11 MU cg16809962 chr16 21566263 11 MU cg10155261 chr16 21566385 11 N cg02066638 chr16 87943038 11 MU cg02672397 chr16 87943158 11 MU cg10110271 chr16 87943182 11 N cg03787864 chr16 88717134 11 MU cg26537639 chr16 88717374 11 N cg04879832 chr16 88717456 11 MU cg08843517 chr16 88717464 11 N cg03125427 chr16 88717482 11 MU cg08177833 chr16 88717537 11 MU cg00054525 chr16 88717587 11 MU cg24046411 chr16 88717646 11 N cg05744675 chr16 88717723 11 N cg12837552 chr17 17687422 11 MU cg15760257 chr17 26699169 11 MU cg05854644 chr17 26699187 11 N cg04880618 chr17 74706567 11 MU cg12296532 chr17 79482181 11 MU cg22076972 chr19 6740812 11 MU cg13341720 chr19 6740888 11 MU cg03705926 chr19 6740952 11 MU cg07046969 chr19 6741049 11 N cg13860006 chr19 6741178 11 MU cg06410591 chr19 6741181 11 N cg05896042 chr19 11450090 11 MU cg02349096 chr19 11450198 11 N cg16206460 chr19 15580700 11 N cg07931411 chr19 15580721 11 MU cg07993547 chr19 18210197 11 MU cg07759570 chr19 35491951 11 MU cg03710719 chr19 36389795 11 MU cg27297851 chr19 36389833 11 MU cg11872300 chr19 42785225 11 MU cg04681368 chr19 42806184 11 N cg10632000 chr19 42806201 11 N cg00524108 chr19 42806203 11 N cg22220467 chr19 42806228 11 MU cg09276315 chr19 47614071 11 MU cg22286978 chr19 58858806 11 MU cg10734734 chr19 58859021 11 MU cg03077836 chr2 9347292 11 MU cg18303215 chr2 44059003 11 N cg04221886 chr2 44059094 11 MU cg20385508 chr2 128180829 11 MU cg03422204 chr2 128180959 11 N cg09747827 chr2 128181313 11 N cg16348316 chr2 128181325 11 MU cg01778114 chr2 216877947 11 MU cg12620499 chr2 216877984 11 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55363058 12 MU cg01317772 chr16 55405200 12 MU cg10235355 chr17 6358830 12 N cg11487526 chr17 6358928 12 MU cg24932788 chr17 70118912 12 N cg01524174 chr17 70119015 12 MU cg21126344 chr17 70119120 12 MU cg20060655 chr19 1748322 12 MU cg11894951 chr19 14168210 12 MU cg00642306 chr19 14168215 12 MU cg11282936 chr19 17007648 12 MU cg24548833 chr19 17007698 12 MU cg09874289 chr19 17008193 12 MU cg08154437 chr19 17008650 12 MU cg05606136 chr19 18336056 12 N cg06907708 chr19 18336077 12 MU cg25383503 chr19 18714552 12 MU cg18111443 chr19 20349275 12 MU cg23679260 chr19 20349303 12 N cg05606351 chr19 54599285 12 MU cg12092201 chr2 43451775 12 N cg16876647 chr2 43451842 12 MU cg08193650 chr2 43451868 12 MU cg17215170 chr2 111874943 12 MU cg05826458 chr2 111875031 12 N cg15680720 chr2 171568412 12 MU cg20389472 chr2 171568526 12 MU cg04257984 chr2 171568984 12 N cg02227919 chr2 171569132 12 MU cg05301383 chr2 171569144 12 N cg22699676 chr2 171569187 12 N cg07363270 chr2 171569282 12 N cg13768137 chr2 177039426 12 N cg12602409 chr2 177039568 12 MU cg14324370 chr2 177042789 12 MU cg03152288 chr2 177042942 12 MU cg09355771 chr2 177043147 12 MU cg13092806 chr2 177043255 12 MU cg02561726 chr2 183943938 12 MU cg13283691 chr2 197852904 12 MU cg18669346 chr20 32308018 12 MU cg18353345 chr21 46961669 12 MU cg22413388 chr22 46367617 12 MU cg13458651 chr22 46367703 12 N cg23060646 chr3 9904411 12 MU cg00498591 chr3 9904557 12 MU cg09469667 chr3 9904687 12 N cg26365925 chr3 44063315 12 N cg16589135 chr3 44063404 12 MU cg07040834 chr3 44063541 12 MU cg23141355 chr3 44063593 12 MU cg09384411 chr3 44063756 12 MU cg17053963 chr3 44063832 12 MU cg07794554 chr3 119041383 12 N cg11099041 chr3 119041385 12 MU cg07074543 chr3 119041529 12 MU cg02650401 chr3 181441501 12 N cg13294849 chr3 181441571 12 MU cg25960893 chr3 181441680 12 N cg02016130 chr4 53578359 12 MU cg10800833 chr4 57521657 12 MU cg12791492 chr4 57522094 12 N cg09853371 chr4 57522145 12 MU cg21899596 chr4 57522493 12 N cg24852548 chr4 57522632 12 MU cg12453905 chr4 154143694 12 MU cg13899188 chr4 154144286 12 MU cg11857997 chr5 3591992 12 MU cg05593349 chr5 10307744 12 MU cg16261871 chr5 59189788 12 N cg11258089 chr5 59189791 12 N cg07190535 chr5 59189843 12 N cg24628676 chr5 59189846 12 N cg08554295 chr5 59189934 12 MU cg07034329 chr5 66124395 12 MU cg09728102 chr5 66124492 12 MU cg17760391 chr5 66124501 12 MU cg10629165 chr5 66124563 12 MU cg20396510 chr5 66124588 12 MU cg13695535 chr5 66124603 12 MU cg03511806 chr5 66124801 12 MU cg07189332 chr5 87439075 12 MU cg10830145 chr5 87440121 12 MU cg09451457 chr5 87440246 12 N cg12436377 chr5 87440296 12 MU cg07100000 chr5 170877840 12 N cg21601919 chr5 170877932 12 MU cg02145102 chr6 31276418 12 N cg18845950 chr6 31276434 12 N cg05419812 chr6 31276437 12 MU cg06414921 chr6 31276504 12 N cg04718845 chr6 152623094 12 MU cg10851835 chr6 152623220 12 N cg05917732 chr6 152623304 12 MU cg02682457 chr6 152623387 12 MU cg12023625 chr6 152623479 12 MU cg07778029 chr7 27205114 12 N cg20741169 chr7 27205159 12 N cg05065989 chr7 27205200 12 MU cg16104915 chr7 27205217 12 N cg18447772 chr7 27205224 12 N cg12600174 chr7 27205230 12 N cg21001184 chr7 27205262 12 N cg16911981 chr7 70061135 12 MU cg03012726 chr7 113722758 12 MU cg19590227 chr1 10755322 13 MU cg04305764 chr1 10755341 13 N cg12426802 chr1 25175293 13 MU cg27537203 chr1 38513245 13 MU cg00385863 chr1 41847974 13 MU cg00290994 chr1 41848739 13 MU cg08037935 chr1 61547340 13 MU cg17017152 chr1 61547389 13 N cg23304296 chr1 116961183 13 N cg07822305 chr1 116961213 13 N cg25743043 chr1 116961233 13 MU cg16596102 chr1 166134346 13 MU cg19160520 chr1 226927217 13 N cg13209122 chr1 226927327 13 MU cg10001282 chr10 71333478 13 MU cg09154558 chr10 71333556 13 N cg25084101 chr10 71336562 13 MU cg05583020 chr10 71336947 13 MU cg24640390 chr10 71336969 13 N cg11471062 chr10 94459183 13 N cg00149473 chr10 94459330 13 MU cg12865381 chr10 94459447 13 N cg11166108 chr11 18416767 13 MU cg11667086 chr11 31848554 13 N cg12500879 chr11 31848649 13 MU cg04145264 chr11 65190825 13 MU cg09411730 chr11 65190946 13 MU cg07985890 chr11 65190999 13 MU cg18019132 chr11 65191707 13 MU cg14758218 chr11 65193528 13 MU cg11990562 chr11 67806526 13 MU cg24030630 chr11 67806668 13 N cg19154027 chr11 101983027 13 MU cg01053411 chr12 12876595 13 MU cg10696085 chr12 50444522 13 MU cg12391352 chr12 99139768 13 MU cg21958524 chr12 99139867 13 N cg22882539 chr12 131438523 13 MU cg20712263 chr12 131438527 13 N cg23379751 chr12 131438540 13 N cg21502466 chr12 131438626 13 N cg04579966 chr13 24846221 13 MU cg09337897 chr13 28394437 13 MU cg00346326 chr13 28394537 13 N cg20945465 chr13 28395271 13 N cg04674803 chr13 28395390 13 MU cg08603188 chr13 28395728 13 MU cg01660473 chr13 28395757 13 MU cg25546248 chr13 28395783 13 N cg20402552 chr13 28397359 13 MU cg04547260 chr13 32605259 13 N cg25667712 chr13 32605332 13 N cg22219254 chr13 32605406 13 MU cg21091247 chr14 69258861 13 MU cg13612175 chr14 69259241 13 MU 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cg01237565 chr14 105715025 14 MU cg18912855 chr14 105830859 14 MU cg02430385 chr15 45421951 14 N cg19295951 chr15 45421998 14 N cg08328777 chr15 45422004 14 N cg24505618 chr15 45422054 14 N cg05804220 chr15 45422062 14 N cg10496082 chr15 45422083 14 MU cg10583297 chr15 45422095 14 N cg24500441 chr15 66274709 14 MU cg00557308 chr15 66274821 14 N cg06100807 chr15 90030016 14 MU cg08641118 chr15 90030046 14 N cg22526139 chr15 90030153 14 N cg01021488 chr15 99395581 14 N cg01224063 chr15 99395633 14 MU cg16427096 chr16 1202468 14 MU cg06277900 chr16 67198029 14 MU cg11996632 chr16 70284893 14 N cg02727363 chr16 70284928 14 MU cg09594067 chr16 70285806 14 MU cg07006158 chr16 70285910 14 N cg14357669 chr16 70285955 14 N cg02602699 chr17 235235 14 MU cg26053771 chr17 8192194 14 N cg15754548 chr17 8192281 14 MU cg20448053 chr17 8192352 14 N cg05151395 chr17 8192371 14 N cg20289346 chr17 37310500 14 MU cg09920557 chr17 61553938 14 MU cg25054907 chr17 61553954 14 N cg02131967 chr17 61554106 14 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cg11631271 chr19 38700487 15 MU cg19221841 chr19 41197049 15 MU cg19643921 chr19 41197106 15 N cg04312358 chr19 41197166 15 N cg23928165 chr19 46996713 15 MU cg11956396 chr19 55591707 15 MU cg01165043 chr2 64994401 15 MU cg18815025 chr2 85980731 15 N cg12930553 chr2 85980742 15 N cg03128635 chr2 85980756 15 MU cg09662694 chr2 85980803 15 N cg15256164 chr2 174220625 15 MU cg20898829 chr20 3229660 15 MU cg03876823 chr20 32899771 15 N cg09250727 chr20 32899784 15 N cg12002708 chr20 32899806 15 MU cg23636941 chr20 32899839 15 N cg19069346 chr21 41239214 15 MU cg25174409 chr21 41239323 15 N cg22413388 chr22 46367617 15 MU cg13458651 chr22 46367703 15 N cg03553786 chr3 13692202 15 N cg23620822 chr3 13692212 15 MU cg08417257 chr3 13692343 15 N cg13707894 chr3 27674461 15 MU cg23439966 chr3 27674472 15 N cg22973319 chr3 50310839 15 MU cg22037648 chr3 50311009 15 MU cg13385156 chr4 1750374 15 N cg12293340 chr4 1750442 15 MU cg16720619 chr4 85423302 15 MU cg18097224 chr5 133451237 15 MU 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chr16 56390830 17 MU cg10383447 chr16 58535093 17 MU cg08092105 chr16 58535384 17 MU cg11640773 chr16 58535416 17 N cg27102864 chr16 58535418 17 N cg02506867 chr16 68121045 17 MU cg19761211 chr16 70415365 17 N cg01269537 chr16 70415514 17 MU cg23669611 chr17 1901684 17 MU cg22298430 chr17 5404330 17 MU cg27230784 chr17 5404337 17 MU cg06462347 chr17 5404508 17 N cg18671949 chr17 5404581 17 MU cg07990843 chr17 25676201 17 MU cg27413732 chr17 27942773 17 MU cg13757826 chr17 27943097 17 MU cg13118906 chr17 61524292 17 MU cg14204735 chr17 61524304 17 N cg09007236 chr17 78228820 17 MU cg26663796 chr17 80255734 17 MU cg08076807 chr17 80255910 17 MU cg01653417 chr17 80256028 17 N cg02759002 chr17 80256046 17 N cg26647303 chr19 2041955 17 N cg00434573 chr19 2041960 17 N cg18418335 chr19 2042088 17 MU cg11894504 chr19 2042391 17 MU cg06908136 chr19 2478866 17 N cg12120729 chr19 2479002 17 MU cg02624855 chr19 41111297 17 MU cg07808231 chr19 41111347 17 MU cg19657814 chr19 47137444 17 MU 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chr21 46352817 19 MU cg11417653 chr22 24181212 19 MU cg25940946 chr22 24181263 19 N cg25037461 chr22 24181268 19 N cg14774086 chr22 24181270 19 N cg23060646 chr3 9904411 19 MU cg00498591 chr3 9904557 19 MU cg09469667 chr3 9904687 19 N cg23350106 chr3 9957221 19 MU cg04066019 chr3 16554466 19 MU cg06085877 chr3 42304966 19 MU cg21327694 chr3 42305063 19 MU cg19612450 chr3 42305186 19 N cg21148362 chr3 62353708 19 MU cg20276377 chr3 99595026 19 MU cg22092811 chr3 99595145 19 MU cg18337222 chr3 99595254 19 N cg23816347 chr3 126113640 19 MU cg09686443 chr3 126113658 19 N cg01815671 chr3 126113663 19 N cg11229862 chr3 126113682 19 N cg20312228 chr3 126113707 19 N cg21109744 chr3 126113743 19 N cg00891278 chr3 126113784 19 N cg00355389 chr3 139653431 19 N cg01755562 chr3 139653545 19 MU cg03458172 chr3 157815672 19 N cg05401764 chr3 157815808 19 MU cg00859352 chr3 171176909 19 N cg23533254 chr3 171176940 19 MU cg20817902 chr4 81124121 19 MU cg25378916 chr5 1875013 19 MU cg06654549 chr5 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cg20490001 chr17 46663873 20 N cg12846139 chr17 79372242 20 N cg22230229 chr17 79372254 20 MU cg23291280 chr19 31847970 20 N cg02128244 chr19 31848008 20 N cg21533331 chr19 31848049 20 MU cg24436715 chr19 31848130 20 N cg19612309 chr19 41112379 20 MU cg17519645 chr19 52206667 20 N cg16854917 chr19 52206691 20 MU cg24879782 chr2 19561482 20 N cg06530338 chr2 19561612 20 MU cg01196788 chr2 19561709 20 N cg23719318 chr2 20865651 20 MU cg19500479 chr2 20865674 20 N cg24539668 chr2 47499521 20 MU cg07669260 chr2 88316847 20 N cg16842767 chr2 88316858 20 MU cg15680720 chr2 171568412 20 MU cg20389472 chr2 171568526 20 N cg27583307 chr2 200320750 20 MU cg07855083 chr2 200320871 20 MU cg22973789 chr2 200321011 20 MU cg17204275 chr2 200321136 20 N cg10515731 chr2 220378996 20 MU cg18844301 chr2 220379000 20 N cg17610929 chr2 220379044 20 MU cg24743778 chr2 220379058 20 N cg19210770 chr2 220379091 20 N cg03387930 chr20 56725833 20 N cg22991320 chr20 56725871 20 N cg19155932 chr20 56725873 20 N 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113699604 20 MU cg11712506 chr5 113699683 20 N cg25108022 chr5 172199313 20 N cg23002268 chr5 172199318 20 N cg09799633 chr5 172199460 20 MU cg09493150 chr5 172199605 20 N cg21570988 chr6 32116841 20 N cg19192280 chr6 32116893 20 N cg02925367 chr6 32116905 20 MU cg17626960 chr6 32116918 20 N cg11945824 chr6 32116963 20 N cg04630292 chr6 32116994 20 N cg24125828 chr6 32117049 20 N cg18111114 chr6 32498493 20 MU cg00587009 chr6 106429129 20 MU cg19509311 chr6 106429191 20 N cg14765959 chr6 106429316 20 MU cg09570682 chr6 106429443 20 MU cg06774797 chr6 132271971 20 MU cg05848863 chr7 16794078 20 MU cg13534901 chr7 17339481 20 MU cg25228422 chr7 19152335 20 MU cg26279021 chr7 19158349 20 MU cg27334919 chr7 19158378 20 N cg16168668 chr7 19158566 20 MU cg14391419 chr7 19158647 20 N cg10126205 chr7 19158664 20 N cg21424940 chr7 19158666 20 N cg04904385 chr7 19158692 20 MU cg10624122 chr7 19158747 20 MU cg23979631 chr7 27142427 20 N cg24058604 chr7 27142437 20 MU cg05921905 chr7 27142527 20 N cg06769202 chr7 27142535 20 N cg02979457 chr7 27142547 20 N cg22943986 chr7 27142700 20 N cg04737131 chr7 27142708 20 MU cg20087093 chr7 27142799 20 N cg03529432 chr7 27187502 20 N cg22469274 chr7 27187553 20 N cg14044640 chr7 27187560 20 MU cg09936824 chr7 27187670 20 N cg12110087 chr7 27187691 20 N cg02384857 chr7 27197614 20 MU cg02000318 chr7 27197713 20 N cg21035142 chr7 151136940 20 N cg06288696 chr7 151137008 20 N cg02512352 chr7 151137048 20 MU cg23273694 chr7 151137162 20 N cg05551889 chr7 151137192 20 N cg11380909 chr8 6658332 20 N cg26356462 chr8 6658407 20 MU cg01917069 chr8 6658537 20 N cg25354466 chr8 65281397 20 N cg18249580 chr8 65281496 20 MU cg11829072 chr8 125463024 20 MU cg25375711 chr8 125463057 20 N cg26547947 chr8 125463066 20 N cg13843266 chr9 38069959 20 MU cg06974755 chr9 135996931 20 MU cg10634619 chr1 3663435 21 N cg15472784 chr1 3663531 21 MU cg25467973 chr1 3663705 21 MU cg05491852 chr1 3663832 21 N cg16007456 chr1 101004638 21 MU cg20302133 chr1 111217194 21 MU cg26013553 chr1 111217406 21 N cg11595545 chr1 111217497 21 MU cg01423964 chr1 111217575 21 N cg10055771 chr1 150254869 21 MU cg08293086 chr1 150266477 21 N cg23819679 chr1 150266488 21 N cg02460371 chr1 150266604 21 MU cg15934674 chr1 150266645 21 N cg05888917 chr1 156626749 21 MU cg00765922 chr1 156626839 21 N cg17154724 chr1 171810322 21 MU cg04399751 chr1 171810376 21 N cg04986015 chr1 171810433 21 N cg02011074 chr1 171810468 21 N cg09557387 chr1 207818395 21 MU cg11752769 chr1 207818423 21 N cg00115980 chr1 207818434 21 N cg26750487 chr1 207818449 21 N cg20744625 chr10 64578469 21 MU cg19704726 chr10 94450056 21 MU cg23727983 chr11 125774082 21 MU cg04609163 chr11 125774090 21 N cg01736784 chr11 125774092 21 N cg20559217 chr11 126173516 21 N cg22911867 chr11 126173548 21 N cg13877106 chr11 126173568 21 N cg01128480 chr11 126173582 21 N cg17494733 chr11 126173585 21 MU cg17462074 chr12 14133168 21 N cg17094171 chr12 14133281 21 MU cg24226973 chr12 14133336 21 N cg13028930 chr12 14133404 21 N cg17203063 chr12 75601711 21 N cg25820699 chr12 75601803 21 MU cg10148473 chr12 75601824 21 N cg16370491 chr12 75601928 21 N cg02818811 chr12 75728458 21 MU cg21087137 chr12 75728469 21 MU cg24234651 chr12 75728471 21 MU cg02478448 chr13 37006063 21 N cg18348647 chr13 37006107 21 MU cg18948722 chr13 37006116 21 N cg05137358 chr13 37006127 21 N cg23316360 chr13 78492916 21 N cg16571983 chr13 78492922 21 N cg06179060 chr13 78493012 21 MU cg04390523 chr13 78493100 21 N cg13818654 chr13 78493126 21 N cg15836660 chr13 78493128 21 N cg01910869 chr13 78493182 21 N cg19916212 chr13 78493203 21 N cg23702615 chr13 78493215 21 N cg18032190 chr13 78493229 21 MU cg22310279 chr13 78493232 21 N cg24236409 chr13 78493282 21 MU cg12602112 chr13 78493294 21 N cg19650157 chr13 78493297 21 N cg13434989 chr13 78493305 21 N cg10016380 chr13 78493313 21 N cg12847373 chr13 78493349 21 N cg19742055 chr13 78493365 21 N cg06057566 chr13 78493561 21 N cg21675115 chr13 78493564 21 N cg26022015 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176963353 25 MU cg26708100 chr2 176983815 25 MU cg10393811 chr2 176983927 25 N cg14142007 chr2 176987225 25 MU cg09968620 chr2 176987383 25 N cg14991487 chr2 176987404 25 N cg04739647 chr2 176987462 25 MU cg05167251 chr2 176987465 25 MU cg22674699 chr2 176987918 25 MU cg22541735 chr2 176988203 25 MU cg14236662 chr2 176988284 25 N cg25984400 chr2 177001883 25 MU cg25903497 chr2 219157103 25 N cg18357696 chr2 219157111 25 N cg08189615 chr2 219157119 25 MU cg06862374 chr2 219736549 25 MU cg11417653 chr22 24181212 25 MU cg25940946 chr22 24181263 25 N cg25037461 chr22 24181268 25 N cg14774086 chr22 24181270 25 N cg23350106 chr3 9957221 25 MU cg26712080 chr3 55519588 25 MU cg00859352 chr3 171176909 25 N cg23533254 chr3 171176940 25 MU cg22546686 chr4 82136823 25 MU cg13798300 chr4 82136839 25 MU cg15972560 chr4 82136940 25 N cg06214925 chr4 109092628 25 N cg12938003 chr4 109092724 25 N cg03149565 chr4 109092769 25 MU cg00597801 chr4 109092918 25 N cg21982950 chr5 1876200 25 N cg07961015 chr5 1876215 25 N cg14564616 chr5 1876337 25 MU cg14451382 chr5 1876397 25 N cg18349911 chr5 1877135 25 MU cg15199326 chr6 2842005 25 N cg19349369 chr6 2842146 25 MU cg14652638 chr6 2842215 25 N cg27056119 chr6 2842239 25 MU cg08542865 chr6 2842248 25 N cg00031256 chr6 2842255 25 MU cg11393995 chr6 2842359 25 N cg09602288 chr6 10382800 25 N cg20168806 chr6 10382828 25 MU cg24336989 chr6 10385903 25 MU cg11811610 chr6 10390811 25 N cg24452128 chr6 10390919 25 MU cg19607165 chr6 10390961 25 N cg17754510 chr6 10391412 25 MU cg05224741 chr6 10422322 25 MU cg24865270 chr6 10422447 25 N cg03204678 chr6 26614535 25 MU cg16519587 chr6 26614649 25 MU cg12831863 chr6 28457210 25 N cg03778669 chr6 28457246 25 MU cg03089717 chr6 28557411 25 N cg15880846 chr6 28557424 25 N cg23490638 chr6 28557467 25 N cg06866163 chr6 28557486 25 MU cg12683342 chr6 28557518 25 N cg13922451 chr6 28557542 25 N cg03470207 chr6 28574981 25 N cg25315731 chr6 28574994 25 MU cg20040488 chr6 28956510 25 N cg24485957 chr6 28956565 25 N cg02766845 chr6 28956577 25 MU cg17545346 chr6 28956609 25 N cg20691507 chr6 28956683 25 N cg16470716 chr6 28956719 25 N cg14449180 chr6 29894619 25 N cg03995122 chr6 29894642 25 MU cg08231349 chr6 29894644 25 N cg00409917 chr6 29894679 25 N cg17217665 chr6 44186914 25 N cg04742345 chr6 44186969 25 N cg11452354 chr6 44187052 25 MU cg10519140 chr6 44187076 25 N cg13793145 chr6 44187109 25 N cg07053014 chr6 44187132 25 N cg05447008 chr6 73331114 25 MU cg11008243 chr6 73331238 25 N cg13332474 chr7 25935147 25 MU cg09339462 chr7 25935223 25 MU cg06206902 chr7 27191226 25 MU cg16771406 chr7 27191352 25 N cg08483376 chr7 27285136 25 MU cg02194223 chr7 27285433 25 N cg01357429 chr7 27285563 25 MU cg19480724 chr7 27285831 25 MU cg22964918 chr7 27286075 25 MU cg01680010 chr7 96647117 25 MU cg01029638 chr7 120628874 25 MU cg11380909 chr8 6658332 25 N cg26356462 chr8 6658407 25 MU cg01917069 chr8 6658537 25 N cg02409878 chr8 99960498 25 N cg26570179 chr8 99960500 25 MU cg19305111 chr8 142428139 25 MU cg00467244 chr8 142428240 25 MU cg26425321 chr8 142428317 25 N cg10634619 chr1 3663435 26 N cg15472784 chr1 3663531 26 MU cg25467973 chr1 3663705 26 MU cg05491852 chr1 3663832 26 N cg07166679 chr1 12123735 26 MU cg16007456 chr1 101004638 26 MU cg20302133 chr1 111217194 26 MU cg26013553 chr1 111217406 26 N cg11595545 chr1 111217497 26 MU cg01423964 chr1 111217575 26 N cg05888917 chr1 156626749 26 MU cg00765922 chr1 156626839 26 N cg08660938 chr1 161510171 26 N cg07706715 chr1 161510194 26 MU cg17154724 chr1 171810322 26 MU cg04399751 chr1 171810376 26 N cg04986015 chr1 171810433 26 N cg02011074 chr1 171810468 26 N cg04932544 chr1 210111403 26 MU cg24428042 chr10 11784408 26 MU cg14032732 chr10 11784473 26 N cg20744625 chr10 64578469 26 MU cg19704726 chr10 94450056 26 MU cg26541003 chr11 9594436 26 MU cg16120811 chr11 9594484 26 N cg02364610 chr11 46317036 26 N cg14547067 chr11 46317154 26 MU cg18877285 chr11 124790815 26 N cg09657963 chr11 124790875 26 MU cg17462074 chr12 14133168 26 N cg17094171 chr12 14133281 26 MU cg24226973 chr12 14133336 26 N cg13028930 chr12 14133404 26 N cg21383810 chr12 65515470 26 MU cg02818811 chr12 75728458 26 MU cg21087137 chr12 75728469 26 MU cg24234651 chr12 75728471 26 MU cg01638592 chr12 111472183 26 MU cg02478448 chr13 37006063 26 N cg18348647 chr13 37006107 26 MU cg18948722 chr13 37006116 26 N cg05137358 chr13 37006127 26 N cg10158541 chr13 37006265 26 N cg17495912 chr13 37006340 26 MU cg09357740 chr14 21131543 26 N cg22895601 chr14 21131621 26 MU cg21915647 chr14 21439301 26 MU cg09790137 chr14 21439440 26 N cg10924691 chr14 21439548 26 MU cg19818764 chr14 21439700 26 MU cg11851456 chr14 23938418 26 N cg18876990 chr14 23938456 26 MU cg06765956 chr14 42074886 26 MU cg26245531 chr14 88459216 26 MU cg03419058 chr15 26108391 26 MU cg16389285 chr15 26108399 26 MU cg20124450 chr15 26108401 26 MU cg22113930 chr15 26108410 26 MU cg26230285 chr15 26108412 26 MU cg14910265 chr15 45670406 26 N cg11431346 chr15 45670478 26 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53315578 26 MU cg27641522 chr18 53447560 26 N cg07519816 chr18 53447596 26 MU cg13216267 chr18 77793342 26 MU cg02401399 chr19 36523584 26 MU cg24027100 chr19 36822549 26 N cg21808240 chr19 36822564 26 N cg02810156 chr19 36822672 26 MU cg22456785 chr19 36822770 26 N cg19406736 chr19 37096010 26 N cg25397945 chr19 37096148 26 MU cg02587316 chr19 37096321 26 N cg05020604 chr19 37096329 26 MU cg07602980 chr19 50815719 26 MU cg05661282 chr19 58220370 26 N cg21790626 chr19 58220494 26 MU cg27049766 chr19 58220516 26 N cg10659886 chr19 58629719 26 N cg08873805 chr19 58629837 26 MU cg14231297 chr19 58629901 26 N cg17408527 chr19 58629967 26 N cg18693673 chr19 58629975 26 N cg01354088 chr2 10183140 26 N cg23458613 chr2 10183227 26 N cg12005760 chr2 10183271 26 MU cg01777397 chr2 29337946 26 N cg07285673 chr2 29337984 26 N cg09695033 chr2 29337988 26 N cg06333058 chr2 29338077 26 MU cg25737323 chr2 29338100 26 MU cg13257636 chr2 29338109 26 N cg23428985 chr2 29338113 26 MU cg23255835 chr2 29338121 26 N cg03135351 chr2 29338258 26 MU cg08808128 chr2 29338432 26 MU cg01648237 chr2 61372066 26 N cg05037927 chr2 61372117 26 N cg16328106 chr2 61372138 26 MU cg19790321 chr2 61372165 26 N cg24757310 chr2 61372226 26 MU cg18158151 chr2 61372256 26 N cg21185289 chr2 74743437 26 MU cg05545132 chr2 144695078 26 MU cg23867997 chr20 61810624 26 MU cg13604020 chr22 38902925 26 MU cg00836482 chr3 119528956 26 MU cg08988364 chr3 143566850 26 MU cg03290752 chr3 176915082 26 MU cg18780412 chr3 179755086 26 MU cg21176048 chr3 179755235 26 N cg02728595 chr3 196255632 26 MU cg14660125 chr4 89378460 26 MU cg26590411 chr4 119199646 26 MU cg03405515 chr4 156297854 26 MU cg03616357 chr4 156297942 26 N cg20334243 chr4 156298002 26 MU cg08015755 chr4 156298050 26 N cg13933262 chr5 140797172 26 MU cg14011639 chr5 140797234 26 N cg10435816 chr5 140797280 26 N cg23563234 chr5 140797286 26 N cg02527199 chr5 175085245 26 MU cg07524827 chr5 175085316 26 N cg12684209 chr6 393110 26 N cg21277995 chr6 393239 26 MU cg27131891 chr6 27513414 26 N cg05310764 chr6 27513479 26 MU cg11623922 chr6 27759386 26 MU cg17438737 chr6 27759399 26 N cg24799187 chr6 27759410 26 N cg18055610 chr6 30095419 26 N cg19059495 chr6 30095495 26 N cg01964152 chr6 30095546 26 N cg08548396 chr6 30095549 26 MU cg12534216 chr6 30095570 26 N cg20383155 chr6 31831428 26 N cg18363192 chr6 31831434 26 N cg27347290 chr6 31831439 26 N cg22753340 chr6 31831489 26 N cg23174932 chr6 31831502 26 MU cg00397479 chr6 31831510 26 N cg17353698 chr6 31831599 26 N cg18857062 chr6 43276478 26 N cg11982736 chr6 43276570 26 N cg27291698 chr6 43276578 26 MU cg21109265 chr6 43276647 26 N cg13097407 chr6 43276658 26 N cg07544038 chr6 71122681 26 MU cg27331241 chr7 751830 26 MU cg00356183 chr7 751833 26 N cg25599475 chr7 853038 26 MU cg02300154 chr7 70597058 26 MU cg01366419 chr7 70597091 26 N cg17314888 chr7 73442282 26 N cg03897712 chr7 73442370 26 MU cg16762778 chr8 27449867 26 MU cg08970347 chr8 27449907 26 MU cg24592790 chr8 27450047 26 N cg22230538 chr8 28258841 26 MU cg13744917 chr1 2986538 27 N cg12267948 chr1 2986566 27 MU cg08640609 chr1 7764642 27 MU cg18086187 chr1 10057158 27 N cg03994053 chr1 10057168 27 N cg23107691 chr1 10057303 27 MU cg27561954 chr1 10057312 27 N cg09820321 chr1 15251269 27 MU cg11141156 chr1 15251288 27 N cg27303880 chr1 64240188 27 N cg00504690 chr1 64240202 27 MU cg06805280 chr1 86046570 27 MU cg04453065 chr1 86046661 27 N cg04997273 chr1 117452884 27 MU cg11221228 chr1 117452905 27 N cg14343890 chr1 228135758 27 MU cg23020486 chr1 236558874 27 MU cg08461107 chr10 30348294 27 N cg12835845 chr10 30348415 27 MU cg12870705 chr10 33624623 27 MU cg18384214 chr10 112836185 27 MU cg16358257 chr10 112836609 27 N cg07560825 chr10 112836681 27 MU cg05238375 chr10 112836780 27 N cg10524152 chr10 112836785 27 N cg26369667 chr11 101981789 27 MU cg18718634 chr11 118023765 27 MU cg21095553 chr11 122753433 27 N cg01591431 chr11 122753443 27 MU cg05445632 chr11 134282404 27 MU cg03853227 chr12 107487365 27 N cg03742214 chr12 107487383 27 MU cg09652427 chr12 107712120 27 MU cg19029509 chr12 107712140 27 N cg08612037 chr12 107712210 27 N cg13882203 chr12 120806178 27 MU cg04101969 chr13 20806477 27 MU cg07148716 chr13 20806539 27 N cg21794428 chr13 21635420 27 MU cg13141192 chr14 21492975 27 MU cg23690444 chr14 81902736 27 MU cg06623560 chr14 81902963 27 MU cg22393360 chr14 81902974 27 N cg06903818 chr14 105444575 27 N cg07828276 chr14 105444724 27 MU cg22696438 chr15 71146679 27 MU cg25959239 chr15 71146682 27 MU cg26653147 chr15 71146819 27 N cg20433014 chr15 75018583 27 N cg01919118 chr15 75018731 27 N cg27051335 chr15 75018733 27 MU cg03797501 chr15 75018774 27 N cg20004910 chr15 75018852 27 N cg02705800 chr15 93199018 27 MU cg25864024 chr15 93199037 27 N cg15289316 chr15 93199049 27 MU cg13784264 chr15 93199130 27 N cg20823977 chr15 93199141 27 N cg07258794 chr16 326049 27 MU cg00609966 chr16 1202819 27 N cg18816877 chr16 1202923 27 MU cg16393726 chr16 56623312 27 N cg08270206 chr16 56623390 27 MU cg02562519 chr16 85202632 27 MU cg19118797 chr17 1613572 27 MU cg25678929 chr17 1613581 27 MU cg19175649 chr17 15164120 27 N cg26388450 chr17 15164133 27 N cg18011045 chr17 15164270 27 MU cg22219136 chr17 15164284 27 N cg16462237 chr17 19770908 27 N cg09795194 chr17 19770910 27 MU cg04611479 chr17 41910366 27 MU cg01147550 chr17 47210027 27 MU cg01429662 chr17 47210162 27 N cg22259831 chr17 47210166 27 N cg18392085 chr17 63556315 27 MU cg18106189 chr18 13218595 27 MU cg21349637 chr18 13218607 27 MU cg19496782 chr18 19749376 27 N cg03473387 chr18 19749395 27 MU cg16485682 chr18 19749540 27 N cg03143886 chr18 22006473 27 N cg24949251 chr18 22006480 27 N cg13865352 chr18 22006621 27 MU cg19083143 chr18 22006626 27 N cg11864882 chr18 43652232 27 N cg17329670 chr18 43652255 27 MU cg21949781 chr18 43652366 27 N cg24026435 chr18 43652398 27 N cg03877492 chr19 1567421 27 N cg13647706 chr19 1567517 27 MU cg12313593 chr19 15619224 27 N cg24456663 chr19 15619329 27 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cg09130162 chr6 47277323 27 MU cg08566247 chr7 12726792 27 N cg07229076 chr7 12726813 27 MU cg16544399 chr7 12726941 27 N cg14169521 chr7 12726944 27 N cg06386517 chr7 50860866 27 N cg00350885 chr7 50860872 27 MU cg04148584 chr7 107643946 27 MU cg26087411 chr7 107643948 27 N cg17657268 chr7 107643956 27 N cg09284719 chr7 107644024 27 N cg21242584 chr8 25041785 27 N cg16826874 chr8 25041796 27 MU cg08949296 chr8 75233153 27 MU cg10594709 chr8 144489858 27 MU cg03939688 chr9 110251706 27 N cg07309102 chr9 110251826 27 MU cg09944147 chr9 116918247 27 MU cg02902865 chr1 12679132 28 MU cg20300343 chr1 149719461 28 N cg00017221 chr1 149719536 28 MU cg02367655 chr1 178064623 28 MU cg04273303 chr1 228225533 28 N cg09227621 chr1 228225606 28 MU cg00078968 chr1 228225659 28 N cg15881990 chr10 13628544 28 MU cg14614563 chr10 32218901 28 MU cg06353720 chr10 91087558 28 MU cg07310984 chr10 91087591 28 MU cg04364339 chr10 91087703 28 MU cg24681845 chr10 126069759 28 MU cg25995133 chr11 407183 28 MU 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chr17 26904150 28 N cg08062469 chr17 26926627 28 N cg06803850 chr17 26926738 28 MU cg22465736 chr17 26926841 28 N cg17960080 chr17 26926868 28 N cg10708793 chr17 40264626 28 N cg27249422 chr17 40264678 28 MU cg22453113 chr17 42287971 28 MU cg22749810 chr17 78237389 28 MU cg14175304 chr17 78237397 28 N cg12221087 chr17 78237432 28 N cg02090762 chr17 79503859 28 MU cg12252135 chr17 79503877 28 MU cg11748640 chr19 5691196 28 MU cg27377863 chr19 16189693 28 MU cg25163134 chr19 40476319 28 MU cg17757000 chr19 40476423 28 N cg24452181 chr19 48984318 28 MU cg05172956 chr2 9283270 28 MU cg01011898 chr2 9283292 28 N cg19482842 chr2 16790314 28 MU cg27115721 chr2 16790338 28 MU cg14640066 chr2 55237421 28 MU cg19254047 chr2 102315097 28 MU cg18295068 chr2 166060400 28 MU cg21382890 chr2 178104794 28 MU cg04741284 chr20 56324226 28 MU cg01582438 chr20 62461159 28 N cg05269938 chr20 62461294 28 MU cg15028052 chr20 62588587 28 MU cg20593868 chr20 62588672 28 N cg01284306 chr21 19190629 28 MU cg13975303 chr22 36850894 28 N cg21844316 chr22 36851007 28 MU cg10455938 chr22 40058150 28 MU cg12511214 chr22 40058293 28 N cg22982767 chr22 46454012 28 MU cg25572043 chr3 15839627 28 MU cg19444609 chr3 44039204 28 MU cg19890879 chr3 44039289 28 N cg07289306 chr3 44039357 28 MU cg03889542 chr3 44039578 28 MU cg17614974 chr3 44039919 28 MU cg12796028 chr3 44040206 28 MU cg26365925 chr3 44063315 28 N cg16589135 chr3 44063404 28 MU cg07040834 chr3 44063541 28 MU cg23141355 chr3 44063593 28 MU cg17219326 chr3 44517846 28 MU cg08477641 chr3 48507508 28 N cg09692335 chr3 48507596 28 N cg26029997 chr3 48507610 28 MU cg19091784 chr3 48507618 28 MU cg01511742 chr3 71112437 28 MU cg20521702 chr3 98452812 28 MU cg13338207 chr3 98452879 28 N cg10128511 chr3 98452940 28 MU cg07390647 chr3 98453086 28 MU cg26072437 chr4 170581380 28 N cg16842643 chr4 170581385 28 MU cg01860063 chr5 100237986 28 MU cg05998153 chr5 134075279 28 MU cg07307388 chr6 25138416 28 MU cg00579036 chr6 25138551 28 N cg12238593 chr6 26440340 28 MU cg00087417 chr6 35887678 28 N cg12215478 chr6 35887710 28 MU cg05917732 chr6 152623304 28 N cg02682457 chr6 152623387 28 MU cg12023625 chr6 152623479 28 N cg27624424 chr6 160112604 28 MU cg13221458 chr6 160112632 28 N cg04358131 chr7 1572252 28 MU cg05279413 chr7 1572327 28 MU cg26468430 chr7 1572571 28 MU cg10884288 chr7 4922196 28 MU cg12600030 chr7 5635820 28 MU cg19928423 chr7 104658369 28 MU cg15571405 chr7 130796167 28 MU cg12577010 chr7 134832544 28 N cg09414827 chr7 134832671 28 MU cg13120814 chr7 134832770 28 MU cg10014112 chr7 134832844 28 N cg06456031 chr7 134832850 28 MU cg07972322 chr7 134832939 28 N cg06201680 chr7 151078646 28 MU cg12775656 chr7 151078750 28 N cg24260847 chr8 37595307 28 N cg01467789 chr8 37595333 28 N cg14036776 chr8 37595363 28 MU cg07538339 chr8 93107298 28 MU cg19007731 chr8 93107394 28 N cg15450160 chr8 99130171 28 MU cg26764250 chr8 104090700 28 MU cg05754853 chr8 116660506 28 MU cg03277455 chr8 116660586 28 N 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113185166 29 MU cg21770330 chr11 124616459 29 MU cg17842066 chr12 7125201 29 MU cg16600991 chr12 54389477 29 MU cg07541701 chr12 54389934 29 N cg14639847 chr12 54389982 29 MU cg19794507 chr12 54391285 29 MU cg09941406 chr12 54391325 29 N cg21837192 chr12 54398765 29 N cg20643952 chr12 54398809 29 MU cg12590051 chr12 54398891 29 N cg03078363 chr12 54408664 29 MU cg16937769 chr12 54409485 29 N cg04704531 chr12 54409491 29 MU cg15817960 chr12 54409599 29 N cg03892356 chr12 54420837 29 MU cg03052128 chr12 85671811 29 N cg07899997 chr12 85671892 29 MU cg01311151 chr12 85671976 29 N cg16413687 chr12 85672623 29 MU cg18041884 chr12 109162555 29 N cg10800464 chr12 109162641 29 MU cg20236513 chr12 109162748 29 N cg27002516 chr12 111835267 29 MU cg14738806 chr12 115102713 29 N cg26620530 chr12 115102726 29 MU cg01765077 chr12 122356316 29 MU cg21171335 chr12 122356390 29 N cg22618164 chr12 122356400 29 N cg09677945 chr13 43597657 29 N cg15988970 chr13 43597736 29 MU cg14789645 chr13 98794353 29 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cg25336332 chr3 24537309 29 MU cg12258785 chr3 24537407 29 MU cg19092164 chr3 45209187 29 N cg13153394 chr3 45209196 29 MU cg15359249 chr3 45209252 29 N cg24284497 chr3 138656356 29 N cg08031151 chr3 138656471 29 MU cg01711746 chr3 138657291 29 MU cg07287724 chr3 138657429 29 N cg02019774 chr3 138658470 29 N cg24625136 chr3 138658554 29 MU cg21575509 chr3 138658704 29 N cg27326452 chr3 138668931 29 MU cg13147822 chr3 168864313 29 MU cg23146833 chr3 171175866 29 MU cg22877570 chr3 171175950 29 MU cg26065488 chr3 171176016 29 N cg00859352 chr3 171176909 29 MU cg23533254 chr3 171176940 29 MU cg19403541 chr4 174438312 29 MU cg11236429 chr4 174459412 29 MU cg18644703 chr5 5887068 29 MU cg23094728 chr5 56110263 29 MU cg22736807 chr5 92938149 29 N cg06820621 chr5 92938232 29 MU cg14477868 chr5 92938260 29 N cg25194822 chr5 113699604 29 MU cg11712506 chr5 113699683 29 N cg10837843 chr5 172199244 29 N cg23162310 chr5 172199255 29 N cg12333707 chr5 172199303 29 N cg25108022 chr5 172199313 29 MU cg23002268 chr5 172199318 29 MU cg09799633 chr5 172199460 29 MU cg09493150 chr5 172199605 29 N cg25412017 chr5 175560449 29 MU cg09533168 chr5 175560568 29 N cg06572420 chr5 175621395 29 MU cg02208657 chr5 175621409 29 N cg04263163 chr5 177371702 29 MU cg23172545 chr6 2875602 29 N cg07096305 chr6 2875723 29 MU cg21570988 chr6 32116841 29 N cg19192280 chr6 32116893 29 N cg02925367 chr6 32116905 29 MU cg17626960 chr6 32116918 29 N cg11945824 chr6 32116963 29 N cg04630292 chr6 32116994 29 N cg24125828 chr6 32117049 29 N cg13534901 chr7 17339481 29 MU cg09864050 chr7 19150174 29 MU cg24496841 chr7 19152017 29 MU cg11973479 chr7 19152073 29 N cg27621129 chr7 19152121 29 N cg25228422 chr7 19152335 29 MU cg26279021 chr7 19158349 29 MU cg27334919 chr7 19158378 29 N cg16168668 chr7 19158566 29 MU cg14391419 chr7 19158647 29 N cg10126205 chr7 19158664 29 N cg21424940 chr7 19158666 29 N cg04904385 chr7 19158692 29 N cg10624122 chr7 19158747 29 MU cg01665174 chr8 119756156 29 MU cg13962186 chr9 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cg13729210 chr5 3606561 30 MU cg22156700 chr5 72732486 30 MU cg02251859 chr5 89854293 30 MU cg11867257 chr5 89854334 30 N cg02792655 chr5 89854364 30 N cg14798760 chr5 89854414 30 N cg08618802 chr5 89854425 30 N cg13441766 chr5 134376442 30 MU cg14994060 chr5 134376489 30 N cg09602288 chr6 10382800 30 N cg20168806 chr6 10382828 30 MU cg00110654 chr6 10385489 30 MU cg24336989 chr6 10385903 30 MU cg11811610 chr6 10390811 30 N cg24452128 chr6 10390919 30 MU cg19607165 chr6 10390961 30 N cg17754510 chr6 10391412 30 MU cg05639246 chr6 10393778 30 MU cg17557766 chr6 10420696 30 N cg00689580 chr6 10420798 30 MU cg10129408 chr6 10421001 30 MU cg13486532 chr6 10421460 30 MU cg10293403 chr6 10421670 30 MU cg22267770 chr6 10422139 30 MU cg06014958 chr6 10425553 30 N cg03074925 chr6 10425625 30 N cg22367191 chr6 10425648 30 MU cg08178940 chr6 50808627 30 N cg09363735 chr6 50808699 30 MU cg03159396 chr6 50808813 30 MU cg25228422 chr7 19152335 30 MU cg07812819 chr7 89975908 30 MU cg05306225 chr8 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201368783 32 N cg21964551 chr1 201368791 32 MU cg27425612 chr1 201368843 32 MU cg23291161 chr1 201368914 32 N cg25947945 chr1 201368946 32 N cg25318301 chr1 228566560 32 MU cg11416243 chr10 11207907 32 MU cg18892808 chr10 11207969 32 N cg00353773 chr10 44880708 32 N cg26718433 chr10 44880730 32 N cg10927719 chr10 44880819 32 MU cg24163616 chr10 100027958 32 MU cg06154084 chr10 102792249 32 MU cg24023187 chr10 102977196 32 N cg12334793 chr10 102977342 32 MU cg10780890 chr10 102978277 32 MU cg05762694 chr10 103990984 32 MU cg07754999 chr10 103991057 32 MU cg00630724 chr10 103991299 32 MU cg20641465 chr10 103991465 32 MU cg23994051 chr10 112432175 32 N cg03520976 chr10 112432321 32 MU cg24456130 chr10 131768029 32 N cg27513573 chr10 131768098 32 MU cg08994045 chr11 2883912 32 MU cg12715569 chr11 20154183 32 MU cg09767822 chr11 20178040 32 N cg08835103 chr11 20178138 32 MU cg05469285 chr11 20178979 32 MU cg14427437 chr11 20229471 32 MU cg16543094 chr11 20229517 32 MU cg23890008 chr11 20229545 32 N cg13139203 chr11 63581631 32 MU cg19311889 chr12 57483793 32 MU cg15126179 chr13 79181338 32 N cg08403195 chr13 79181440 32 MU cg19981568 chr14 60977300 32 MU cg14282221 chr14 101121018 32 N cg02480144 chr14 101121093 32 N cg14500250 chr14 101121140 32 MU cg24015081 chr14 105144033 32 MU cg22044901 chr15 68126292 32 MU cg25903779 chr15 68128025 32 MU cg14210607 chr15 68128175 32 N cg02454890 chr15 90544015 32 MU cg00133595 chr16 31228059 32 MU cg10038009 chr16 31228202 32 MU cg13352836 chr16 31228263 32 N cg08346922 chr16 31228299 32 N cg27656614 chr16 81479309 32 MU cg06251016 chr16 89258571 32 MU cg01465169 chr17 27944849 32 MU cg08377924 chr17 41363628 32 N cg19548479 chr17 41363737 32 N cg24940138 chr17 41363741 32 MU cg21211480 chr17 41363774 32 N cg15026277 chr17 41363789 32 N cg03049782 chr17 41363891 32 N cg18878992 chr17 43974344 32 MU cg19156875 chr17 43974446 32 N cg11849086 chr17 62776530 32 MU cg19472078 chr17 77020166 32 MU cg18996910 chr17 77020220 32 MU cg08349573 chr17 77020243 32 MU cg24844534 chr17 77020267 32 N cg18009376 chr17 80056900 32 MU cg05896042 chr19 11450090 32 MU cg02349096 chr19 11450198 32 N cg00523012 chr19 13264324 32 MU cg24125901 chr19 18716439 32 MU cg04072597 chr19 18716499 32 N cg10617994 chr19 18716527 32 N cg23635599 chr19 45281006 32 N cg01057656 chr19 45281140 32 MU cg18612786 chr19 45281236 32 N cg16529268 chr19 45281284 32 N cg04434147 chr19 45281287 32 N cg25253217 chr19 47776728 32 MU cg24083817 chr19 49217255 32 MU cg20495404 chr2 8823649 32 MU cg04525491 chr2 8823683 32 MU cg01184784 chr2 8823756 32 MU cg06946452 chr2 119606631 32 N cg24001710 chr2 119606695 32 N cg26990102 chr2 119606746 32 MU cg09385093 chr2 119607885 32 MU cg08138685 chr2 119610526 32 MU cg17342925 chr2 119610605 32 N cg01469887 chr2 175205716 32 N cg09344183 chr2 175205785 32 N cg27078140 chr2 175205821 32 N cg10828660 chr2 175205852 32 MU cg22306928 chr2 219866424 32 MU cg00215432 chr2 219866437 32 MU cg00218620 chr2 219866460 32 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126455885 33 MU cg10839291 chr11 126456008 33 N cg10695848 chr12 48206783 33 MU cg02408775 chr12 48206848 33 MU cg23588713 chr12 56523270 33 MU cg21334598 chr12 117316490 33 MU cg02823783 chr12 124246919 33 MU cg01240444 chr12 124247047 33 N cg11026555 chr12 124247223 33 MU cg27234864 chr13 21295941 33 MU cg04370314 chr13 100643312 33 MU cg22489321 chr13 100643388 33 N cg18477204 chr14 70038884 33 MU cg02380334 chr14 70038925 33 N cg14539393 chr14 70038949 33 N cg19567295 chr14 89884237 33 N cg12790592 chr14 89884273 33 MU cg23411981 chr14 105940377 33 MU cg03451296 chr14 105940391 33 MU cg06100807 chr15 90030016 33 MU cg08641118 chr15 90030046 33 N cg22526139 chr15 90030153 33 N cg01003448 chr16 745685 33 N cg07482202 chr16 745687 33 MU cg01195246 chr16 745765 33 N cg10504751 chr16 56390830 33 MU cg04313875 chr17 1553202 33 MU cg15380836 chr17 1553341 33 N cg12608565 chr17 4802692 33 N cg05529816 chr17 4802735 33 N cg14275842 chr17 4802813 33 MU cg07834574 chr17 4802847 33 MU cg14482741 chr17 4802906 33 MU cg20814095 chr17 4802981 33 MU cg21071097 chr17 4803053 33 N cg26695387 chr17 21189768 33 N cg15416179 chr17 21189859 33 MU cg14862207 chr17 36715798 33 MU cg10188897 chr17 42392436 33 MU cg03061122 chr17 42392559 33 MU cg01122302 chr17 42392668 33 N cg11088422 chr17 42392697 33 MU cg17083506 chr17 48226252 33 MU cg05735639 chr17 49021875 33 N cg17129986 chr17 49022021 33 MU cg00023001 chr17 55938748 33 N cg22451358 chr17 55938871 33 MU cg06452419 chr17 55938940 33 N cg12605662 chr18 56935199 33 MU cg04583149 chr19 1207124 33 MU cg10094078 chr19 1467925 33 N cg03306486 chr19 1467952 33 MU cg19333963 chr19 1467979 33 MU cg04314308 chr19 19336150 33 N cg25814383 chr19 19336240 33 MU cg03096975 chr19 46142574 33 N cg07804711 chr19 46142647 33 MU cg12536502 chr19 46142673 33 MU cg07749923 chr19 46142711 33 N cg01544351 chr19 46142757 33 MU cg22893791 chr2 26407628 33 N cg10927841 chr2 26407743 33 MU cg20289949 chr2 43019614 33 N cg20857709 chr2 43019727 33 MU cg26360792 chr2 43019997 33 N cg27299406 chr2 43020013 33 MU cg09480054 chr2 43020129 33 N cg08738430 chr2 43452561 33 MU cg25954914 chr2 54785183 33 MU cg15543284 chr2 54785307 33 N cg07850967 chr2 54785550 33 MU cg02271560 chr2 55450737 33 N cg11652636 chr2 55450823 33 N cg20042692 chr2 55450874 33 MU cg18462521 chr2 64879911 33 MU cg11009335 chr2 166651299 33 MU cg25492645 chr2 166651320 33 N cg02764346 chr2 166651375 33 N cg10413944 chr2 224702734 33 MU cg09456094 chr2 231280878 33 MU cg11827097 chr2 231280903 33 N cg11702456 chr2 231280907 33 MU cg20067688 chr20 30449097 33 MU cg14745622 chr20 61447686 33 N cg20331689 chr20 61447729 33 N cg15266508 chr20 61447742 33 N cg16653692 chr20 61447749 33 N cg16847645 chr20 61447790 33 N cg22489878 chr20 61447803 33 N cg27206421 chr20 61447818 33 N cg16132351 chr20 61447823 33 MU cg18291290 chr20 61447879 33 N cg07845648 chr20 61447915 33 N cg18014624 chr20 61447929 33 N cg00612595 chr21 47717864 33 MU cg00815214 chr21 47717953 33 N cg05611779 chr22 24552379 33 MU cg25247183 chr22 38073049 33 N cg06822952 chr22 38073178 33 MU cg04689080 chr22 46263207 33 MU cg17200465 chr3 40428508 33 N cg00656387 chr3 40428638 33 MU cg13409248 chr3 40428643 33 N cg10755058 chr3 40428713 33 MU cg18306943 chr3 40428807 33 N cg03355998 chr3 147124363 33 MU cg21127068 chr3 147124417 33 N cg16209664 chr3 147131146 33 N cg12204258 chr3 147131178 33 MU cg08160350 chr4 996052 33 N cg05327835 chr4 996175 33 MU cg04508482 chr4 996280 33 N cg08844887 chr4 1006120 33 N cg17105609 chr4 1006237 33 MU cg08774969 chr4 1006240 33 MU cg20564913 chr4 1006270 33 MU cg05866411 chr4 1006278 33 MU cg17807131 chr4 4854690 33 MU cg13752974 chr4 4854786 33 N cg13084458 chr4 128553917 33 N cg11779273 chr4 128553930 33 MU cg15437651 chr4 128554035 33 N cg07174665 chr4 129474594 33 N cg17521583 chr4 129474618 33 MU cg15225701 chr5 108083455 33 MU cg09565404 chr5 108083489 33 N cg04337096 chr5 146257347 33 N cg20560075 chr5 146257484 33 MU cg12232308 chr6 30711580 33 N cg13337949 chr6 30711586 33 N cg17389738 chr6 30711642 33 N cg20178172 chr6 30711655 33 MU cg08384314 chr6 30711680 33 MU cg13804182 chr6 30711796 33 N cg16493531 chr6 30711802 33 MU cg10916998 chr6 30711832 33 N cg00011994 chr6 30711904 33 N cg18353226 chr6 33280390 33 N cg14419102 chr6 33280400 33 N cg06375761 chr6 33280408 33 N cg26083458 chr6 33280424 33 N cg13638257 chr6 33280436 33 N cg05700142 chr6 33280454 33 N cg01654446 chr6 33280476 33 N cg06230948 chr6 33280518 33 MU cg03591496 chr6 33280653 33 N cg12215478 chr6 35887710 33 N cg17639265 chr6 35887853 33 MU cg23514619 chr6 43237330 33 MU cg04537567 chr6 99296068 33 MU cg14712531 chr6 108485043 33 MU cg24472375 chr6 108485102 33 N cg09236658 chr6 108485152 33 N cg06103642 chr6 108496716 33 MU cg11086764 chr6 108496811 33 N cg21762523 chr6 108497717 33 MU cg03673965 chr6 108497734 33 N cg19672993 chr7 70160734 33 MU cg05650680 chr8 1765217 33 N cg25673948 chr8 1765296 33 N cg11998141 chr8 1765309 33 N cg09177567 chr8 1765312 33 N cg09899173 chr8 1765336 33 MU cg15821939 chr8 1765387 33 N cg16481961 chr8 1765421 33 N cg00790020 chr8 1765477 33 N cg10191684 chr8 22423014 33 MU cg07924703 chr8 22423020 33 MU cg07469555 chr8 22423055 33 MU cg10698928 chr8 65290320 33 MU cg14156441 chr8 126447096 33 MU cg14425564 chr9 126794772 33 N cg14071579 chr9 126794830 33 MU cg11453712 chr9 139741803 33 N cg14245947 chr9 139741866 33 MU cg09513309 chr1 6614775 34 MU cg23394391 chr1 8409065 34 MU cg21449655 chr1 15851444 34 MU cg06703079 chr1 46597625 34 MU cg23660154 chr1 110626540 34 MU cg14313310 chr1 184006816 34 N cg00044463 chr1 184006933 34 MU cg06371502 chr1 197879766 34 MU cg04868078 chr1 197898229 34 MU cg17869355 chr1 207038496 34 MU cg08479073 chr1 207038584 34 N cg08313638 chr10 12110577 34 MU cg07262635 chr10 18689378 34 N cg25327888 chr10 18689440 34 MU cg12289251 chr10 18689471 34 N cg00579859 chr10 60026291 34 MU cg02616947 chr10 102822508 34 MU cg05855489 chr10 104503620 34 N cg12846190 chr10 104503666 34 MU cg14838188 chr10 104503687 34 MU cg02230593 chr10 119293083 34 N cg26288991 chr10 119293207 34 MU cg04421971 chr10 119293341 34 N cg09176256 chr10 119293967 34 MU cg23087306 chr10 119294055 34 N cg07833262 chr10 119296182 34 N cg20792376 chr10 119296297 34 MU cg06141846 chr10 119296305 34 N cg06456154 chr10 119310356 34 N cg15526081 chr10 119310404 34 MU cg08451832 chr10 119310964 34 MU cg09334277 chr10 119312077 34 MU cg03667544 chr10 135049598 34 MU cg11190277 chr11 69455213 34 N cg26399164 chr11 69455217 34 N cg00858899 chr11 69455277 34 N cg07781399 chr11 69455287 34 N cg03735308 chr11 69455331 34 MU cg12825880 chr11 69455671 34 MU cg12162138 chr11 78673073 34 MU cg17542408 chr11 78673100 34 N cg16235582 chr11 78673212 34 N cg03910065 chr11 115372874 34 MU cg10553088 chr11 115372912 34 N cg14030346 chr11 115372975 34 N cg25364859 chr12 50222202 34 MU cg07541701 chr12 54389934 34 N cg14639847 chr12 54389982 34 MU cg21837192 chr12 54398765 34 N cg20643952 chr12 54398809 34 MU cg12590051 chr12 54398891 34 N cg18540674 chr12 54405379 34 MU cg03078363 chr12 54408664 34 MU cg16937769 chr12 54409485 34 N cg04704531 chr12 54409491 34 MU cg15817960 chr12 54409599 34 N cg04576672 chr12 54422239 34 N cg27441225 chr12 54422350 34 MU cg11941633 chr12 54422447 34 MU cg22378817 chr12 54422488 34 N cg18041884 chr12 109162555 34 N cg10800464 chr12 109162641 34 MU cg20236513 chr12 109162748 34 N cg01765077 chr12 122356316 34 MU cg21171335 chr12 122356390 34 N cg22618164 chr12 122356400 34 N cg25930673 chr12 123319894 34 N cg22842854 chr12 123319900 34 MU cg05034702 chr13 61989701 34 MU cg15839219 chr13 61989724 34 N cg17903776 chr13 61989726 34 N cg15182360 chr13 61989737 34 N cg12721730 chr13 61989817 34 N cg09296044 chr15 43477606 34 MU cg04864199 chr15 45493327 34 MU cg17911021 chr15 45493416 34 N cg23855392 chr15 80189782 34 MU cg16718263 chr15 96596349 34 MU cg13920934 chr15 96596478 34 N cg00167654 chr17 5973536 34 MU cg13484608 chr17 21191452 34 MU cg06862673 chr17 37617707 34 MU cg14131285 chr17 71949210 34 MU cg08661469 chr17 72732823 34 N cg01944585 chr17 72732912 34 N cg23838375 chr17 72732946 34 N cg09852601 chr17 72732955 34 N cg10817887 chr17 72732964 34 MU cg24166450 chr17 74248466 34 MU cg16472334 chr17 77721757 34 N cg18933840 chr17 77721824 34 MU cg26141063 chr18 32870200 34 MU cg27662412 chr18 39535095 34 MU cg04622103 chr19 1812664 34 N cg11251319 chr19 1812732 34 MU cg00523012 chr19 13264324 34 MU cg23291280 chr19 31847970 34 N cg02128244 chr19 31848008 34 N cg21533331 chr19 31848049 34 MU cg24436715 chr19 31848130 34 N cg09961571 chr19 39421871 34 MU cg15779837 chr19 48918116 34 MU cg23444265 chr19 48918205 34 N cg02184280 chr19 49954649 34 MU cg24879782 chr2 19561482 34 N cg06530338 chr2 19561612 34 MU cg01196788 chr2 19561709 34 N cg16851771 chr2 42360170 34 MU cg21550141 chr2 102439950 34 MU cg15680720 chr2 171568412 34 N cg20389472 chr2 171568526 34 MU cg07466807 chr2 173326947 34 MU cg26112014 chr2 230135789 34 N cg14165051 chr2 230135879 34 MU cg07472438 chr2 230135929 34 MU cg20003983 chr20 56725730 34 N cg03387930 chr20 56725833 34 N cg22991320 chr20 56725871 34 MU cg19155932 chr20 56725873 34 N cg09663111 chr20 56725876 34 N cg25906442 chr20 56725887 34 MU cg07655948 chr20 56725899 34 N cg18562935 chr20 56726000 34 N cg12430776 chr21 19191096 34 MU cg21608237 chr22 45148229 34 MU cg09954385 chr22 45148285 34 N cg13996698 chr22 45148306 34 N cg27526665 chr3 24537050 34 N cg27622506 chr3 24537160 34 MU cg21303011 chr3 24537177 34 N cg00323305 chr3 24537182 34 N cg27388911 chr3 24537277 34 N cg16579438 chr3 24537301 34 N cg25336332 chr3 24537309 34 N cg06853609 chr3 37218771 34 MU cg17573813 chr3 61237223 34 MU cg18153137 chr3 71114771 34 MU cg19477084 chr3 126006762 34 N cg16342439 chr3 126006835 34 N cg14310703 chr3 126006903 34 MU cg01709619 chr3 129346694 34 MU cg02568670 chr3 141173927 34 MU cg04401986 chr4 81189927 34 MU cg16551732 chr4 169799087 34 MU cg03729337 chr5 122430019 34 N cg27087956 chr5 122430153 34 MU cg02564291 chr5 122430235 34 N cg19328475 chr5 122430277 34 N cg25412017 chr5 175560449 34 MU cg09533168 chr5 175560568 34 N cg06572420 chr5 175621395 34 MU cg02208657 chr5 175621409 34 MU cg04263163 chr5 177371702 34 MU cg23172545 chr6 2875602 34 N cg07096305 chr6 2875723 34 MU cg00799826 chr6 30071200 34 MU cg20797270 chr6 30071288 34 N cg13074835 chr6 31515196 34 MU cg10714284 chr6 32908901 34 MU cg16300030 chr6 32908980 34 N cg20392842 chr6 32909003 34 N cg18630040 chr6 46702983 34 MU cg26705953 chr6 46703123 34 N cg26953727 chr6 134176231 34 MU cg10308833 chr6 134176248 34 MU cg25228422 chr7 19152335 34 MU cg16168668 chr7 19158566 34 MU cg14391419 chr7 19158647 34 N cg10126205 chr7 19158664 34 N cg21424940 chr7 19158666 34 N cg04904385 chr7 19158692 34 N cg14604568 chr7 32110523 34 MU cg15988350 chr7 32110650 34 N cg03808172 chr7 96339361 34 MU cg26619624 chr7 96339434 34 N cg26269703 chr7 149119681 34 MU cg11382963 chr8 72754469 34 MU cg16531209 chr8 99951696 34 N cg03020554 chr8 99951730 34 MU cg09825146 chr8 125462982 34 N cg26141173 chr8 125463007 34 N cg11829072 chr8 125463024 34 MU cg25375711 chr8 125463057 34 N cg26547947 chr8 125463066 34 N cg26372060 chr8 145691254 34 MU cg14614793 chr9 111695728 34 MU cg04043571 chr9 127265989 34 MU

TABLE 2 Top 100 hypo-methylated sites per tissue/cell type Illumina Tissue/Cell MU/ Probe Name Chr. Position Type N cg07033722 chr1 40539032 1 MU cg17481936 chr1 116371802 1 N cg23952578 chr1 116371871 1 MU cg11939496 chr1 160833560 1 MU cg08538581 chr1 162500634 1 MU cg15288326 chr1 200994098 1 MU cg04851465 chr1 212408609 1 MU cg24250902 chr1 230415547 1 MU cg03144619 chr1 230415668 1 N cg16660547 chr10 114574152 1 MU cg05923857 chr10 114911615 1 MU cg24788483 chr10 114911652 1 MU cg16636767 chr11 13694647 1 MU cg00009088 chr11 60930188 1 MU cg13980609 chr11 66883117 1 MU cg05463966 chr11 67803951 1 N cg12070987 chr11 67804055 1 MU cg04664897 chr11 72925852 1 MU cg15212210 chr11 118986793 1 N cg22259797 chr11 118986860 1 MU cg06980932 chr11 121301233 1 MU cg08428868 chr11 123323626 1 MU cg27366072 chr12 12222913 1 MU cg10456459 chr12 22843015 1 MU cg12866960 chr12 66635398 1 MU cg13500029 chr12 75873039 1 MU cg17183174 chr13 76115526 1 MU cg03313271 chr13 113913094 1 MU cg19502671 chr13 114129004 1 MU cg23514375 chr13 114129039 1 MU cg01024962 chr14 31389792 1 MU cg26298914 chr14 68798365 1 MU cg12655112 chr15 42261154 1 MU cg04354689 chr16 2660830 1 MU cg03571959 chr16 2660876 1 N cg00504410 chr16 4732262 1 N cg03963853 chr16 4732369 1 MU cg07248377 chr16 4732406 1 N cg07737292 chr16 56892460 1 MU cg01735503 chr16 69966815 1 N cg03549146 chr16 69966902 1 MU cg00158530 chr16 69966973 1 N cg08209934 chr16 69966975 1 N cg03126713 chr16 89299480 1 MU cg06988336 chr16 89922397 1 MU cg01739831 chr16 89922539 1 N cg08036492 chr17 13976536 1 MU cg13093111 chr17 66308577 1 MU cg20610950 chr17 75096202 1 MU cg11597902 chr17 75096239 1 N cg11186858 chr17 75096382 1 MU cg02675920 chr17 78747934 1 N cg11222173 chr17 78748019 1 N cg11153071 chr17 78748077 1 MU cg26783127 chr17 79128918 1 MU cg16732654 chr17 79129022 1 N cg23334433 chr17 79129051 1 N cg20429104 chr18 74114570 1 MU cg12019801 chr18 74114708 1 N cg14976569 chr19 4667105 1 MU cg26538782 chr19 10308540 1 MU cg25517015 chr19 17584020 1 MU cg10480329 chr2 27011850 1 MU cg04781916 chr2 48013473 1 MU cg06373940 chr2 128052778 1 MU cg14306819 chr2 128052889 1 N cg15633603 chr2 190399258 1 MU cg02796279 chr2 208101334 1 MU cg12884009 chr2 219079038 1 MU cg14012546 chr2 233981788 1 N cg22666015 chr2 233981885 1 MU cg01112020 chr2 239008986 1 N cg09039163 chr2 239009036 1 N cg15871206 chr2 239009118 1 MU cg11231069 chr2 240225142 1 MU cg06978145 chr20 30754792 1 MU cg04217515 chr21 46325853 1 MU cg11327657 chr21 46388162 1 MU cg08425796 chr22 50981121 1 MU cg02244028 chr3 38992200 1 MU cg06585734 chr3 46037942 1 MU cg03171795 chr3 70882921 1 MU cg19533955 chr3 99790878 1 MU cg17086773 chr3 112486072 1 MU cg18990407 chr3 184297380 1 MU cg15334250 chr3 184297495 1 N cg27214730 chr3 184297522 1 N cg13403369 chr3 195868640 1 MU cg15044270 chr3 196065485 1 N cg13314965 chr3 196065506 1 N cg05445326 chr3 196065569 1 MU cg00828556 chr3 196351986 1 MU cg25771026 chr3 196352027 1 N cg14786790 chr4 89766654 1 MU cg20107506 chr4 100874826 1 MU cg11802666 chr4 185345707 1 MU cg17500228 chr5 448790 1 MU cg22185977 chr5 1518133 1 MU cg05573654 chr5 1518179 1 N cg20942286 chr5 79366549 1 MU cg10967114 chr5 150442668 1 MU cg05141695 chr6 341180 1 MU cg13719228 chr6 341315 1 N cg05612654 chr6 2375895 1 MU cg01300914 chr6 2763797 1 N cg20017123 chr6 2763919 1 MU cg18128312 chr6 25463672 1 MU cg18835472 chr6 26546056 1 MU cg10718056 chr6 28884599 1 MU cg00259404 chr6 28885568 1 MU cg06545464 chr6 32299649 1 N cg20966357 chr6 32299676 1 MU cg01026383 chr6 32905012 1 N cg20978247 chr6 32905085 1 MU cg19268453 chr6 32905114 1 N cg01374870 chr6 32905127 1 N cg04903089 chr6 32905190 1 N cg07918933 chr6 72051803 1 MU cg06297318 chr6 127667691 1 MU cg04531756 chr6 135516912 1 N cg02127509 chr6 135516936 1 N cg04739200 chr6 135517046 1 MU cg00235484 chr6 136825415 1 MU cg12458039 chr7 210075 1 MU cg03663120 chr7 2284600 1 MU cg05861879 chr7 2284723 1 N cg15704521 chr7 2773877 1 MU cg03310874 chr7 4850260 1 MU cg21932542 chr7 4850345 1 N cg22528270 chr7 151505116 1 MU cg15310871 chr8 20077936 1 MU cg11699257 chr8 28634455 1 MU cg07023317 chr8 28961315 1 N cg25596405 chr8 28961333 1 MU cg09322573 chr8 28961356 1 MU cg08444833 chr8 141574162 1 N cg07466463 chr8 141574207 1 MU cg06517984 chr8 143407646 1 MU cg17117981 chr8 144410972 1 MU cg13931640 chr9 137277819 1 MU cg23087108 chr1 1120617 2 MU cg24250902 chr1 230415547 2 N cg03144619 chr1 230415668 2 MU cg07629149 chr10 334731 2 N cg23844018 chr10 334773 2 MU cg09848445 chr10 43601862 2 MU cg14725719 chr10 43601916 2 N cg11199827 chr10 49881056 2 MU cg03969079 chr10 63759909 2 MU cg25131632 chr10 94549040 2 MU cg17995557 chr10 126289971 2 MU cg24517899 chr10 126290093 2 N cg07652788 chr10 133598589 2 MU cg18961589 chr10 133598669 2 N cg22697239 chr11 44626708 2 MU cg01199327 chr11 44626750 2 MU cg18476690 chr11 61125255 2 N cg14331899 chr11 61125360 2 MU cg00464927 chr11 68139121 2 MU cg13738327 chr11 68139233 2 MU cg08648997 chr11 68139350 2 N cg17382048 chr11 118754386 2 N cg16235962 chr11 118754507 2 N cg04625873 chr11 118754530 2 N cg25087423 chr11 118754535 2 MU cg26164712 chr11 118754565 2 N cg16280667 chr11 118754593 2 N cg05921138 chr11 121440849 2 N cg17232476 chr11 121440880 2 MU cg16931707 chr12 42873202 2 MU cg25683989 chr12 111124215 2 MU cg17955329 chr12 113528764 2 N cg19631815 chr12 113528791 2 MU cg26426437 chr12 121686502 2 MU cg16085171 chr12 123569118 2 MU cg04356090 chr12 129280133 2 MU cg05934015 chr12 129280265 2 MU cg16782719 chr12 129280334 2 N cg02742418 chr12 129280388 2 MU cg24102222 chr12 129280449 2 N cg14622549 chr12 132549292 2 MU cg21703988 chr12 132549404 2 N cg19172447 chr12 132551483 2 MU cg24980032 chr12 132551592 2 N cg23755933 chr12 133349383 2 MU cg19590591 chr12 133349418 2 MU cg04828493 chr13 111328783 2 MU cg26053795 chr14 103373907 2 MU cg15861806 chr14 103374045 2 N cg13164814 chr14 103468473 2 N cg20043258 chr14 103468582 2 MU cg20908276 chr14 106134542 2 MU cg14936008 chr14 107260014 2 MU cg01666796 chr15 70364327 2 MU cg12349571 chr15 70364359 2 MU cg00181849 chr16 920856 2 MU cg27634195 chr16 1440421 2 N cg09717923 chr16 1440498 2 MU cg04696391 chr16 3021794 2 MU cg06323049 chr16 28943094 2 N cg27565966 chr16 28943198 2 MU cg05433111 chr16 28943232 2 N cg01758575 chr16 28943288 2 N cg00709979 chr16 85866348 2 MU cg09201499 chr16 88838113 2 MU cg09437994 chr16 89035147 2 MU cg03797660 chr16 89035228 2 N cg06800849 chr16 89180587 2 MU cg06255609 chr17 3493563 2 N cg02212339 chr17 3493666 2 MU cg17441377 chr17 3906640 2 MU cg01798813 chr17 3906674 2 N cg01884715 chr17 73316454 2 N cg06943385 chr17 73316483 2 N cg03717570 chr17 73316496 2 MU cg00900735 chr17 73316505 2 MU cg07044414 chr17 73316622 2 N cg06091647 chr17 78899392 2 MU cg14467066 chr17 79419739 2 N cg11699517 chr17 79419796 2 MU cg21373806 chr17 79419834 2 MU cg18050634 chr17 79420145 2 MU cg21284015 chr17 79420246 2 N cg24550880 chr17 79420279 2 N cg23881099 chr17 79924020 2 MU cg08817507 chr17 79924068 2 MU cg08732805 chr17 79924176 2 N cg27579854 chr17 80818992 2 N cg11872040 chr17 80819028 2 MU cg08811443 chr17 80819108 2 N cg14579184 chr17 80873365 2 MU cg19755815 chr17 80873572 2 N cg02092514 chr17 80873656 2 MU cg18092363 chr18 77202678 2 MU cg26615224 chr19 1621124 2 MU cg15533809 chr19 2076775 2 MU cg16743273 chr19 2076833 2 N cg24299136 chr19 2511707 2 MU cg00827581 chr19 10434088 2 MU cg00041047 chr19 49828629 2 MU cg01453814 chr19 49828641 2 MU cg24188017 chr19 49828658 2 N cg06237487 chr19 49828667 2 N cg21843262 chr2 44513757 2 MU cg24724513 chr2 44513905 2 N cg10854528 chr2 54855934 2 MU cg19846040 chr2 55015049 2 MU cg07558704 chr2 73286489 2 MU cg07015784 chr2 219257760 2 MU cg01591579 chr2 234111145 2 MU cg09799039 chr2 240213875 2 MU cg07835236 chr20 62549301 2 N cg10842070 chr20 62549359 2 MU cg05898333 chr20 62549385 2 N cg00791190 chr22 50223573 2 MU cg07680505 chr22 50223663 2 N cg23683962 chr3 24549503 2 MU cg15035590 chr3 66437456 2 MU cg11160673 chr3 107240475 2 MU cg05480110 chr3 195534854 2 MU cg03653856 chr3 197500859 2 MU cg03732753 chr4 1834583 2 N cg17574471 chr4 1834678 2 MU cg27024271 chr4 39447968 2 N cg26800884 chr4 39448008 2 MU cg23730277 chr4 102734752 2 MU cg00762029 chr4 185317438 2 MU cg21964798 chr5 1503259 2 MU cg12670963 chr5 1511422 2 N cg11576486 chr5 1511473 2 MU cg16256592 chr5 175664221 2 N cg02027263 chr5 175664224 2 MU cg03680932 chr6 16306683 2 MU cg26088561 chr6 30619080 2 N cg18048542 chr6 30619113 2 N cg01676996 chr6 30619167 2 N cg01152616 chr6 30619173 2 N cg13584784 chr6 30619190 2 MU cg05143092 chr6 30619209 2 N cg19849557 chr6 30619242 2 MU cg18067520 chr6 107114594 2 MU cg16079645 chr7 1039875 2 N cg21248060 chr7 1039957 2 MU cg27205928 chr7 1062652 2 MU cg16492851 chr7 1062681 2 N cg04682977 chr7 1062711 2 N cg25315312 chr7 2020528 2 MU cg20943353 chr7 2020583 2 N cg11020333 chr7 2861855 2 MU cg16586807 chr7 2948081 2 MU cg17768768 chr7 2948123 2 MU cg06567596 chr7 97832830 2 MU cg13659700 chr7 97832841 2 N cg07006818 chr7 97832945 2 N cg02368706 chr8 1892271 2 MU cg04909834 chr8 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chr8 29344567 11 N cg18557212 chr8 29344583 11 MU cg05973488 chr8 29344599 11 N cg01911981 chr8 39380341 11 N cg17527589 chr8 39380435 11 MU cg19598713 chr8 144601734 11 N cg18210511 chr8 144601781 11 N cg09580859 chr8 144601800 11 N cg25900150 chr8 144601851 11 MU cg00783706 chr8 144601883 11 N cg00110724 chr8 144885851 11 MU cg03000485 chr8 144973638 11 MU cg17740743 chr1 1986220 12 N cg12391964 chr1 1986275 12 MU cg01421021 chr1 10719929 12 MU cg13377965 chr1 21844799 12 MU cg08713722 chr1 38942976 12 MU cg19358202 chr1 41486102 12 MU cg17589319 chr1 43530194 12 MU cg07602008 chr1 86048479 12 N cg20293609 chr1 86048540 12 MU cg17499345 chr1 86048585 12 N cg16563929 chr10 525898 12 N cg00195144 chr10 525946 12 MU cg27201679 chr10 1120794 12 N cg11314310 chr10 1120831 12 MU cg13473894 chr10 13771383 12 MU cg12656653 chr10 13771427 12 MU cg23873415 chr10 13771465 12 MU cg18594033 chr10 112524504 12 MU cg06874891 chr10 112524550 12 N cg21193639 chr11 2349510 12 MU cg23267171 chr11 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cg16996571 chr14 91691129 12 MU cg19873420 chr14 91691179 12 N cg14304073 chr14 91691190 12 MU cg16434156 chr14 94497084 12 MU cg10984327 chr14 105683798 12 N cg13692222 chr14 105683925 12 MU cg04835431 chr14 105684005 12 MU cg20800039 chr15 58891233 12 MU cg22389121 chr15 80217380 12 MU cg06717750 chr16 667157 12 MU cg27060386 chr16 667188 12 N cg06013113 chr16 678127 12 MU cg01201782 chr16 1652449 12 N cg10141942 chr16 1652482 12 N cg02654411 chr16 1652552 12 MU cg06780216 chr16 2374397 12 N cg16463165 chr16 2374474 12 N cg07753939 chr16 2374515 12 MU cg00794055 chr16 2544909 12 MU cg20364187 chr16 3139060 12 MU cg07651316 chr16 3641320 12 MU cg07351059 chr16 29999179 12 MU cg09251197 chr16 30000254 12 MU cg09969487 chr16 49902325 12 MU cg08138817 chr16 49902342 12 N cg05819154 chr16 49902436 12 N cg02667487 chr16 67920036 12 MU cg08520423 chr16 68013968 12 MU cg23401756 chr16 68013981 12 N cg17248540 chr16 69143500 12 N cg04320956 chr16 69143512 12 MU cg02730714 chr16 69143775 12 MU 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236777077 17 MU cg27276994 chr2 236777122 17 N cg19504661 chr2 239542427 17 MU cg09549949 chr2 240240119 17 MU cg00397274 chr2 240240189 17 N cg11960435 chr20 31434982 17 MU cg24531396 chr22 36013560 17 N cg01945840 chr22 36013675 17 MU cg15161225 chr22 36013678 17 N cg01815090 chr22 36013708 17 N cg15212895 chr22 49064512 17 MU cg19458233 chr3 100148679 17 MU cg13592780 chr3 123010034 17 MU cg14696064 chr3 123010055 17 N cg10843210 chr3 195613589 17 N cg15010544 chr3 195613641 17 N cg09628877 chr3 195613739 17 MU cg14030878 chr3 195615302 17 N cg04214520 chr3 195615359 17 N cg18473978 chr3 195615386 17 MU cg02296904 chr4 877321 17 MU cg11174998 chr4 47885045 17 MU cg16316054 chr4 185480838 17 N cg19745553 chr4 185480871 17 MU cg17678847 chr4 186933824 17 N cg20468325 chr4 186933893 17 MU cg10896676 chr4 187517794 17 MU cg03064793 chr4 187517847 17 N cg03211527 chr5 1121021 17 MU cg08436224 chr5 1122753 17 N cg00395291 chr5 1122815 17 MU cg12301921 chr5 1122879 17 N cg27656498 chr5 14492820 17 N cg19800026 chr5 14492945 17 MU cg02379684 chr5 154181831 17 MU cg23939866 chr5 154181852 17 N cg16577707 chr5 154181866 17 N cg01114405 chr6 504249 17 MU cg16199907 chr6 504351 17 MU cg18185178 chr6 1516613 17 MU cg01682157 chr6 1516643 17 N cg09088836 chr6 2510144 17 N cg02666566 chr6 2510173 17 MU cg17947992 chr6 2510180 17 MU cg27215236 chr7 878040 17 MU cg23656906 chr7 878067 17 N cg07815491 chr7 878073 17 N cg06293719 chr7 889564 17 MU cg11852646 chr7 1956546 17 MU cg08722586 chr7 2046821 17 N cg03043709 chr7 2046920 17 MU cg08305371 chr7 2046963 17 MU cg17760049 chr7 4774448 17 MU cg22153931 chr7 43917675 17 MU cg23043230 chr7 97935666 17 N cg15211734 chr7 97935756 17 N cg10592494 chr7 97935764 17 MU cg06466782 chr7 101049300 17 MU cg01380946 chr7 101049372 17 N cg06823965 chr7 101632645 17 MU cg12937183 chr7 151421916 17 MU cg04058237 chr7 156337107 17 N cg21839504 chr7 156337168 17 MU cg18195333 chr7 156337209 17 N cg18753341 chr7 157202405 17 MU cg19839956 chr8 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cg14588422 chr10 35081084 18 MU cg15247093 chr10 47768476 18 MU cg09287096 chr10 86998622 18 MU cg05848463 chr10 88982562 18 N cg07505719 chr10 88982663 18 MU cg25467549 chr10 88982701 18 N cg04426802 chr10 126314680 18 MU cg26725552 chr10 126314758 18 N cg07984614 chr10 126314798 18 N cg27607136 chr11 1857442 18 MU cg22348345 chr11 1857477 18 N cg14642259 chr11 47359017 18 MU cg05649391 chr11 47359115 18 N cg01176141 chr11 70023032 18 MU cg13089599 chr11 70023060 18 MU cg24832721 chr11 70023142 18 N cg03614381 chr11 71229642 18 MU cg14632485 chr11 113216820 18 MU cg13659360 chr11 121023627 18 MU cg24327383 chr11 121023708 18 N cg13751188 chr11 121023720 18 N cg06193412 chr11 134608672 18 MU cg09414985 chr11 134608774 18 N cg10457436 chr12 6745871 18 MU cg04701618 chr12 131443231 18 MU cg19026802 chr12 131443260 18 N cg13251533 chr12 132286017 18 MU cg10130374 chr13 111935412 18 N cg22795345 chr13 111935508 18 MU cg14270002 chr13 111935521 18 MU cg22612590 chr13 114187973 18 N cg13241681 chr13 114187990 18 MU cg20539175 chr13 114188051 18 N cg03535253 chr14 93706571 18 MU cg00705661 chr14 93706609 18 MU cg22920967 chr14 104047722 18 N cg00834537 chr14 104047751 18 MU cg18471471 chr15 100537686 18 MU cg07015368 chr15 100537761 18 N cg16628135 chr16 1397791 18 MU cg06698598 chr16 1398226 18 MU cg06739469 chr16 29343859 18 N cg00711542 chr16 29343894 18 MU cg00535785 chr16 49766785 18 MU cg08842616 chr16 70733772 18 N cg08796240 chr16 70733832 18 MU cg02501155 chr16 79263473 18 MU cg10221896 chr16 79263595 18 N cg03430116 chr16 85083564 18 MU cg04922015 chr16 86500429 18 MU cg00284420 chr16 87311948 18 MU cg01579218 chr16 87312047 18 N cg01753252 chr16 88757588 18 MU cg06359492 chr16 89162377 18 MU cg10158369 chr16 89162475 18 N cg09482780 chr17 7756609 18 MU cg01218720 chr17 40322138 18 N cg26583088 chr17 40322250 18 MU cg00439089 chr17 48263150 18 N cg16514513 chr17 48263159 18 N cg11615029 chr17 48263189 18 N cg11993636 chr17 48263198 18 N cg25735490 chr17 48263204 18 MU cg26758486 chr17 71533286 18 MU cg08459186 chr17 71533337 18 N cg01449677 chr17 76794888 18 MU cg04117396 chr17 76795028 18 N cg17602102 chr17 78796159 18 MU cg14073057 chr17 78796363 18 MU cg25949961 chr17 79166676 18 MU cg10963033 chr17 79166715 18 N cg20632978 chr17 79167828 18 MU cg01977863 chr17 79167836 18 N cg04460362 chr17 79913303 18 MU cg25105415 chr17 79913327 18 MU cg03654588 chr17 79913374 18 N cg23515921 chr17 80747189 18 N cg18602928 chr17 80747240 18 MU cg04934595 chr17 81021419 18 MU cg14515064 chr17 81021472 18 N cg04516518 chr18 77193464 18 N cg07759042 chr18 77193502 18 MU cg12524531 chr18 77193581 18 N cg24175580 chr19 36024217 18 N cg27036341 chr19 36024225 18 MU cg21453378 chr19 36024253 18 N cg06710596 chr19 36024279 18 N cg15729137 chr19 36024282 18 N cg25681177 chr19 36024313 18 N cg25498045 chr19 36024375 18 N cg02457644 chr19 36245325 18 MU cg02384543 chr19 58879857 18 MU cg03781262 chr19 58879871 18 N cg08084812 chr19 58879943 18 N cg19361181 chr2 10616043 18 MU cg26539736 chr2 10666585 18 MU cg10767193 chr2 10666662 18 N cg08846959 chr2 10666713 18 N cg00138926 chr2 11118414 18 N cg07702693 chr2 11118564 18 MU cg07622575 chr2 11487955 18 MU cg24586870 chr2 233198590 18 MU cg17365725 chr2 242698748 18 N cg02698622 chr2 242698811 18 MU cg25478712 chr21 44840120 18 MU cg20341238 chr21 44865764 18 MU cg22247792 chr21 47548991 18 MU cg25663764 chr22 19965534 18 MU cg22808478 chr22 20073393 18 N cg08706141 chr22 20073403 18 N cg07797030 chr22 20073477 18 N cg22786486 chr22 20073528 18 MU cg06964030 chr22 20073636 18 N cg05347334 chr4 2439397 18 MU cg26149658 chr4 2439423 18 N cg15689795 chr4 2439478 18 N cg17924901 chr4 3475233 18 N cg05673770 chr4 3475328 18 N cg21333208 chr4 3475372 18 MU cg14378364 chr4 123591475 18 MU cg04311686 chr5 1179328 18 MU cg03887092 chr6 1080885 18 MU cg00197993 chr6 1201056 18 MU cg09088836 chr6 2510144 18 N cg02666566 chr6 2510173 18 MU cg17947992 chr6 2510180 18 MU cg02115397 chr6 32975850 18 N cg10620866 chr6 32975875 18 N cg18477188 chr6 32975885 18 MU cg16673446 chr6 32975925 18 N cg24696151 chr6 32975986 18 N cgl4144933 chr6 32975994 18 N cg07294785 chr6 32976018 18 N cg18015809 chr6 47202254 18 N cg21552001 chr6 47202394 18 MU cg17356964 chr6 106582638 18 N cg25581448 chr6 106582669 18 MU cg08112248 chr6 169002003 18 MU cg14806083 chr6 169002120 18 N cg02152290 chr7 551601 18 MU cg15879716 chr7 1932272 18 MU cg03653726 chr7 2769253 18 MU cg06779606 chr7 3041079 18 MU cg20037968 chr7 3041098 18 N cg02751532 chr7 3041110 18 N cg15319585 chr7 4307392 18 N cg07005020 chr7 4307433 18 MU cg09404689 chr7 4307490 18 N cg13621882 chr7 5647885 18 N cg20407483 chr7 5647920 18 MU cg18753341 chr7 157202405 18 MU cg06286135 chr7 157511479 18 MU cg14584961 chr7 157533065 18 MU cg25899922 chr7 158934015 18 N cg09909417 chr7 158934031 18 MU cg26431815 chr8 668053 18 MU cg16830075 chr8 142151154 18 MU cg14056656 chr9 132892163 18 MU cg23887776 chr1 2827798 19 N cg15356461 chr1 2827833 19 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19 MU cg13189671 chr12 123750782 19 N cg14790739 chr12 123750827 19 MU cg25368917 chr12 124788627 19 MU cg01656620 chr12 124788698 19 N cg18672389 chr12 124789036 19 N cg14273093 chr12 124789096 19 MU cg06674275 chr13 113430153 19 N cg12572352 chr13 113430266 19 MU cg26433554 chr14 23315189 19 N cg14414203 chr14 23315249 19 MU cg19678259 chr14 23315264 19 N cg20384325 chr15 49268750 19 MU cg06332339 chr15 73889528 19 MU cg08435157 chr15 73889561 19 N cg20238681 chr15 94443233 19 MU cg07701166 chr15 94443237 19 MU cg06349762 chr16 888938 19 MU cg01125617 chr16 889011 19 N cg08333708 chr16 2535757 19 MU cg01447429 chr16 29222265 19 MU cg04453552 chr16 57394387 19 MU cg17248540 chr16 69143500 19 N cg04320956 chr16 69143512 19 MU cg02751838 chr16 75148330 19 N cg00004883 chr16 75148460 19 MU cg21289738 chr17 8383742 19 N cg21871213 chr17 8383800 19 MU cg02059952 chr17 8384595 19 N cg06557376 chr17 8384607 19 MU cg10533978 chr17 40842804 19 MU cg24136288 chr17 43183150 19 MU cg02314166 chr17 60767131 19 MU cg09237133 chr17 80278914 19 MU cg09340279 chr17 80278947 19 N cg02775417 chr19 643457 19 MU cg12062672 chr19 643555 19 N cg07962882 chr19 1117289 19 MU cg09432775 chr19 1117453 19 MU cg05919982 chr19 1117571 19 N cg22018329 chr19 3116716 19 MU cg18030386 chr19 4944160 19 MU cg21993464 chr19 14041305 19 MU cg14697425 chr19 14042840 19 MU cg26008272 chr19 45574917 19 N cg15732530 chr19 45575023 19 MU cg24030434 chr19 45575134 19 N cg23633937 chr19 45575142 19 N cg08531052 chr19 49633795 19 N cg11881562 chr19 49633902 19 MU cg23226510 chr19 56042469 19 N cg00844791 chr19 56042579 19 MU cg26858540 chr19 56603322 19 MU cg14385008 chr2 3469154 19 MU cg06529996 chr2 3469212 19 N cg20210057 chr2 98351150 19 N cg21585640 chr2 98351171 19 MU cg25138365 chr2 98351315 19 N cg04707348 chr2 98351548 19 MU cg08042027 chr2 240040089 19 MU cg07215298 chr2 240040219 19 MU cg25921544 chr2 240040290 19 N cg21478921 chr2 242617935 19 MU cg03301433 chr2 242617946 19 N cg03877418 chr2 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chr6 33288785 19 N cg09864211 chr6 33288800 19 N cg15070677 chr6 33288804 19 MU cg14523639 chr6 33288839 19 N cg18704865 chr6 33288851 19 N cg07098566 chr6 33288891 19 MU cg03243551 chr6 33288941 19 N cg13059373 chr6 33288944 19 N cg14199833 chr6 33288964 19 MU cg00117005 chr6 33288995 19 N cg09883659 chr6 164520746 19 N cg25862072 chr6 164520845 19 MU cg10365469 chr6 168417097 19 MU cg19252565 chr6 168417126 19 N cg11387916 chr6 169273332 19 N cg05740106 chr6 169273346 19 MU cg20876525 chr6 169273382 19 N cg11792186 chr7 2083105 19 N cg18483265 chr7 2083181 19 N cg15914316 chr7 2083197 19 MU cg02527886 chr7 2968316 19 MU cg10939949 chr7 2968406 19 N cg24506706 chr7 4169485 19 N cg08250787 chr7 4169604 19 MU cg13818127 chr7 4169630 19 MU cg07673838 chr7 4169752 19 N cg21080473 chr7 50560524 19 MU cg27394114 chr7 157261038 19 N cg06518327 chr7 157261163 19 MU cg16708264 chr7 157336976 19 MU cg13323391 chr7 157579745 19 MU cg18064706 chr7 157646656 19 MU cg14628604 chr7 157646701 19 N 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27 MU cg04741516 chr14 99786208 27 N cg10538275 chr14 99786226 27 N cg10533694 chr14 105181370 27 MU cg11290775 chr14 105181449 27 N cg01921845 chr14 105238643 27 MU cg10415607 chr14 105238745 27 N cg14184729 chr14 105238773 27 N cg24765648 chr14 105349455 27 MU cg07883696 chr15 41628806 27 MU cg18083415 chr15 99669970 27 N cg22245494 chr15 99669982 27 MU cg00701514 chr16 1183462 27 MU cg07812849 chr16 1183588 27 N cg00180470 chr16 11343465 27 MU cg09328356 chr16 29913162 27 MU cg02488299 chr16 29913223 27 N cg27156649 chr16 30567019 27 MU cg04453552 chr16 57394387 27 MU cg16559361 chr16 88543440 27 MU cg26884787 chr16 88543447 27 N cg04228827 chr16 88550365 27 N cg02071579 chr16 88550450 27 MU cg00379559 chr16 88564934 27 N cg10095533 chr16 88565080 27 MU cg06800849 chr16 89180587 27 MU cg10016717 chr17 88246 27 MU cg11751810 chr17 88286 27 MU cg06932713 chr17 910196 27 MU cg19463982 chr17 48614436 27 MU cg25648211 chr17 76312455 27 MU cg17838182 chr17 76991208 27 MU cg25052374 chr17 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chr12 133120548 28 N cg05053403 chr12 133120676 28 MU cg01362762 chr13 114805400 28 MU cg26070281 chr14 21560028 28 MU cg01813877 chr14 103369717 28 MU cg22779765 chr14 103369786 28 N cg18993641 chr14 106109496 28 MU cg13175736 chr15 62537871 28 N cg06136125 chr15 62537916 28 N cg23066057 chr15 62537934 28 MU cg25637473 chr15 62537960 28 MU cg17274072 chr15 90904227 28 MU cg01201782 chr16 1652449 28 N cg10141942 chr16 1652482 28 N cg02654411 chr16 1652552 28 MU cg09692355 chr16 1677047 28 MU cg03929747 chr16 2282618 28 MU cg10278046 chr16 4802848 28 N cg16755475 chr16 4802990 28 MU cg09596260 chr16 50338380 28 MU cg09100343 chr16 57147202 28 N cg06880930 chr16 57147300 28 MU cg10464529 chr16 58030730 28 MU cg00454856 chr16 58030811 28 MU cg26673975 chr16 66420974 28 MU cg02924834 chr16 87744876 28 N cg27459530 chr16 87744906 28 MU cg08734237 chr16 87744948 28 MU cg24352934 chr17 1053717 28 MU cg15893929 chr17 3755190 28 MU cg10924857 chr17 20013829 28 MU cg05888362 chr17 20013887 28 N cg21784143 chr17 20460588 28 MU cg05058886 chr17 20460627 28 N cg23674202 chr17 80541549 28 MU cg24220178 chr17 80541649 28 N cg25899954 chr18 8660164 28 MU cg13513411 chr19 4676202 28 MU cg24868883 chr19 45648302 28 MU cg00673724 chr19 45648483 28 N cg13786513 chr19 45648562 28 MU cg15147841 chr19 46033285 28 MU cg11276053 chr19 46307734 28 MU cg02359773 chr19 46307846 28 MU cg20469261 chr19 50361793 28 MU cg26570040 chr19 50361827 28 N cg23985239 chr19 50361910 28 N cg11616411 chr19 54290799 28 N cg24823137 chr19 54290902 28 N cg05882315 chr19 54290905 28 N cg20644425 chr19 54290913 28 MU cg08239565 chr19 54290996 28 N cg01975502 chr19 54291017 28 N cg18403970 chr19 54291050 28 N cg01743020 chr2 1886086 28 MU cg09927251 chr2 1886105 28 N cg17162031 chr2 1886127 28 N cg02381965 chr2 3418480 28 MU cg24929896 chr2 3418530 28 N cg03239335 chr2 3418641 28 MU cg03445173 chr2 17998337 28 MU cg03269045 chr2 26680478 28 MU cg03427577 chr2 61809639 28 MU cg00102512 chr2 129103162 28 N cg10933428 chr2 129103164 28 MU cg01125214 chr2 236888769 28 MU cg04216051 chr2 240172612 28 MU cg20415945 chr2 240172645 28 N cg17257554 chr2 242004824 28 MU cg18624016 chr2 242004954 28 N cg15707332 chr20 32436359 28 MU cg04893833 chr20 32436391 28 N cg17894293 chr20 32436418 28 N cg00171166 chr20 35444275 28 MU cg06814048 chr20 35444364 28 N cg00734813 chr20 35444512 28 N cg21970473 chr20 35444558 28 N cg08209184 chr20 35444560 28 MU cg19412478 chr22 26883640 28 MU cg25931138 chr3 195709698 28 MU cg12923728 chr3 195709715 28 N cg26124466 chr3 197347235 28 N cg00133223 chr3 197347256 28 N cg02746684 chr3 197347276 28 MU cg11985754 chr4 1214965 28 N cg12389346 chr4 1215112 28 MU cg11920453 chr4 1411820 28 MU cg00313498 chr4 7787944 28 MU cg10099518 chr4 7787960 28 MU cg27567206 chr4 7787983 28 MU cg06664054 chr4 83323522 28 N cg18579761 chr4 83323585 28 MU cg07979388 chr4 169573255 28 MU cg00612828 chr4 183815971 28 MU cg09557149 chr4 183816164 28 MU cg19945912 chr4 186945215 28 N cg09227533 chr4 186945339 28 MU cg10427811 chr5 79647590 28 MU cg24526056 chr5 156479556 28 MU cg12943317 chr5 156479607 28 N cg26994283 chr5 177964757 28 MU cg24830898 chr5 177964873 28 N cg06024039 chr6 25726884 28 N cg07077277 chr6 25726912 28 N cg10093738 chr6 25726947 28 N cg25090472 chr6 25726953 28 MU cg11761622 chr6 25726956 28 N cg13778975 chr6 25727019 28 N cg03347739 chr6 53787668 28 MU cg02655824 chr6 167051714 28 N cg02118390 chr6 167051744 28 MU cg25296804 chr6 167051781 28 N cg14764357 chr7 1093718 28 N cg26071556 chr7 1093854 28 MU cg11116288 chr7 1093880 28 N cg13320257 chr7 1093903 28 N cg19016273 chr7 3948024 28 MU cg24872851 chr7 4265468 28 N cg19854900 chr7 4265529 28 N cg19727491 chr7 4265548 28 MU cg18365383 chr7 33725819 28 MU cg23940732 chr7 33725885 28 N cg01312316 chr7 33725924 28 N cg20985897 chr7 157778531 28 MU cg26480662 chr7 157778593 28 N cg13083427 chr7 157778645 28 N cg16481556 chr8 2196764 28 MU cg24196612 chr8 21608193 28 N cg15997512 chr8 21608254 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63676485 29 N cg06982473 chr11 63676611 29 MU cg09034795 chr11 63676760 29 N cg17676132 chr11 63886204 29 MU cg18093064 chr11 63886229 29 N cg04815835 chr11 64522197 29 MU cg12071131 chr11 64522266 29 N cg27402782 chr11 65296794 29 MU cg16516712 chr11 70557445 29 MU cg23055735 chr11 70557464 29 N cg11609668 chr11 117075078 29 MU cg13909661 chr12 26386833 29 MU cg11724984 chr12 121890864 29 N cg02827029 chr12 121890907 29 MU cg00634652 chr13 112166963 29 MU cg21561701 chr13 114193150 29 MU cg03479204 chr13 114193155 29 MU cg24926364 chr13 114748575 29 N cg00738309 chr13 114748624 29 MU cg12441624 chr13 114748733 29 N cg01515732 chr14 76012927 29 MU cg13738571 chr14 76012941 29 N cg00292073 chr16 1187671 29 MU cg07205796 chr16 1187722 29 MU cg06328338 chr16 1511419 29 MU cg26923629 chr16 4799203 29 MU cg06480482 chr16 29265720 29 MU cg02227036 chr16 50425329 29 MU cg01986767 chr16 50425351 29 MU cg05922453 chr16 50425414 29 N cg27239168 chr16 58192136 29 MU cg27238852 chr16 58192174 29 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chr7 2144579 29 MU cg16904636 chr7 4857015 29 N cg13972986 chr7 4857070 29 MU cg06947694 chr7 5389271 29 MU cg19547972 chr7 73389575 29 N cg07993468 chr7 73389632 29 MU cg12548057 chr7 73389660 29 MU cg06746318 chr7 101755186 29 MU cg18541042 chr7 140227195 29 MU cg13675721 chr7 140227273 29 MU cg03383886 chr7 156336617 29 MU cg08789101 chr7 156336675 29 N cg04058237 chr7 156337107 29 N cg21839504 chr7 156337168 29 MU cg18195333 chr7 156337209 29 N cg14353728 chr8 140937350 29 N cg21602882 chr8 140937410 29 MU cg09541794 chr8 144872852 29 MU cg14528751 chr9 91144074 29 MU cg17006413 chr1 942451 30 N cg21472517 chr1 942471 30 MU cg18019572 chr1 942489 30 N cg22673826 chr1 942498 30 N cg01922485 chr1 942696 30 MU cg16097329 chr1 3384827 30 N cg24371534 chr1 3384929 30 MU cg02328660 chr1 3389744 30 MU cg03050669 chr1 3606777 30 N cg23163013 chr1 3606892 30 MU cg16401490 chr1 10704953 30 MU cg22347357 chr1 10705035 30 N cg15074789 chr1 16451437 30 MU cg20288836 chr1 87995129 30 MU 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chr17 1799551 30 N cg14896608 chr17 1799628 30 MU cg09021817 chr17 1799727 30 N cg25588480 chr17 4763240 30 MU cg13483263 chr17 7493150 30 N cg13159929 chr17 7493222 30 MU cg24060527 chr17 9066156 30 MU cg20705392 chr17 9066278 30 N cg12601118 chr17 9859123 30 MU cg09123026 chr17 74480528 30 MU cg09013599 chr17 75207115 30 N cg04308797 chr17 75207259 30 MU cg17940464 chr17 75207380 30 N cg01449677 chr17 76794888 30 MU cg04117396 chr17 76795028 30 N cg22949575 chr17 79948825 30 MU cg22528521 chr19 1424814 30 N cg19593255 chr19 1424952 30 MU cg00766482 chr19 6269688 30 MU cg07838427 chr19 10665928 30 MU cg14697425 chr19 14042840 30 MU cg19726935 chr19 17691610 30 N cg10730370 chr19 17691691 30 MU cg26961332 chr19 18420631 30 MU cg20535805 chr19 18420735 30 N cg26011534 chr19 54972426 30 N cg11320517 chr19 54972544 30 MU cg10814803 chr2 63273940 30 MU cg24586870 chr2 233198590 30 MU cg26126749 chr2 239970075 30 MU cg08781549 chr2 239970152 30 N cg26641275 chr2 239971554 30 MU cg13626676 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64120354 32 N cg14476630 chr11 72437731 32 MU cg23249399 chr11 72437871 32 N cg05824174 chr12 3142845 32 MU cg19862427 chr12 111358545 32 N cg09763086 chr12 111358601 32 N cg23533926 chr12 111358616 32 N cg01197294 chr12 111358627 32 N cg04735123 chr12 111358639 32 MU cg04243901 chr12 125441307 32 MU cg25769122 chr12 132939657 32 N cg10426234 chr12 132939776 32 MU cg19444998 chr12 133352427 32 N cg22683624 chr12 133352552 32 MU cg21074877 chr14 23481968 32 MU cg15361373 chr14 70316621 32 MU cg23319601 chr14 70316638 32 MU cg02973354 chr14 70316670 32 N cg03991106 chr15 101661445 32 MU cg13480902 chr15 101661529 32 N cg22551145 chr15 101661580 32 N cg26619740 chr16 971556 32 N cg08214990 chr16 971579 32 MU cg02333298 chr16 27052228 32 N cg04172166 chr16 27052337 32 MU cg03790192 chr16 88044375 32 N cg16537589 chr16 88044443 32 MU cg19809575 chr16 88044501 32 N cg06320420 chr16 88872998 32 MU cg10155891 chr16 88941576 32 MU cg03233548 chr16 88949985 32 N cg08351911 chr16 88950075 32 MU cg01168962 chr16 88950197 32 MU cg00957684 chr16 88962800 32 MU cg06153386 chr16 89764255 32 N cg08013914 chr16 89764279 32 MU cg02128278 chr16 89764314 32 N cg00627045 chr17 11461244 32 N cg11471805 chr17 11461316 32 MU cg19586165 chr17 37814072 32 N cg00129232 chr17 37814104 32 MU cg10533978 chr17 40842804 32 MU cg09739017 chr17 65040854 32 N cg01541398 chr17 65040983 32 MU cg04777612 chr17 73805953 32 MU cg05647756 chr17 73805997 32 MU cg26525457 chr17 76672843 32 MU cg27169819 chr17 79388690 32 MU cg20428133 chr17 79388814 32 N cg26066277 chr17 79792777 32 MU cg23708206 chr17 79792925 32 N cg25885449 chr18 77638415 32 MU cg16488953 chr18 77638446 32 MU cg09414987 chr19 392121 32 MU cg13426839 chr19 539489 32 MU cg17124430 chr19 793925 32 MU cg23292266 chr19 11285128 32 N cg07676300 chr19 11285258 32 MU cg10389930 chr19 11285331 32 N cg01596520 chr19 14225029 32 N cg19586199 chr19 14225172 32 MU cg09776718 chr19 30164820 32 MU cg23620904 chr19 30165018 32 N cg08427305 chr19 30165133 32 MU cg19421371 chr19 45211155 32 MU cg26347064 chr19 45211248 32 N cg04770020 chr2 1544321 32 MU cg06574769 chr2 1544352 32 N cg18995558 chr2 1544370 32 N cg07608848 chr2 1647185 32 N cg15796818 chr2 1647197 32 MU cg00138926 chr2 11118414 32 N cg07702693 chr2 11118564 32 MU cg16145071 chr2 101727897 32 MU cg26650973 chr2 128350306 32 N cg20679271 chr2 128350442 32 MU cg01577298 chr2 128350572 32 N cg05689415 chr2 234386055 32 MU cg24414737 chr2 242616833 32 N cg02823437 chr2 242616875 32 N cg05880944 chr2 242616965 32 MU cg15734942 chr2 242617028 32 N cg19513282 chr20 62200711 32 N cg23957118 chr20 62200850 32 MU cg21520607 chr20 62200854 32 N cg11815682 chr21 44486381 32 N cg11796095 chr21 44486452 32 MU cg00775094 chr21 44904824 32 N cg04550439 chr21 44904941 32 MU cg27462210 chr21 44905012 32 N cg12955277 chr21 44905064 32 N cg05670717 chr21 46368052 32 N cg11988476 chr21 46368090 32 N cg00660655 chr21 46368151 32 MU cg14026113 chr22 48977177 32 N cg22504180 chr22 48977277 32 MU cg04377908 chr22 48977382 32 N cg07047131 chr3 12856650 32 N cg03181573 chr3 12856729 32 N cg18797950 chr3 12856746 32 N cg15730967 chr3 12856795 32 MU cg02217386 chr3 12856862 32 N cg24097241 chr3 71247280 32 MU cg15556723 chr3 71247394 32 MU cg06856329 chr3 71247442 32 MU cg23186787 chr4 4380004 32 MU cg17069541 chr4 4380019 32 N cg08577953 chr4 100574455 32 MU cg12097672 chr4 100574527 32 N cg09176893 chr4 100574596 32 N cg00428692 chr5 194530 32 N cg26466258 chr5 194607 32 MU cg25522232 chr5 195132 32 MU cg24883374 chr5 195171 32 N cg00084435 chr5 195276 32 N cg11024341 chr5 2041368 32 MU cg06460652 chr5 176519331 32 MU cg27320658 chr5 176519389 32 N cg20725716 chr5 176519455 32 N cg20768313 chr5 179267916 32 MU cg26844954 chr6 26867420 32 MU cg02820566 chr7 187254 32 N cg20865618 chr7 187333 32 MU cg25980689 chr7 187339 32 MU cg24883607 chr7 609506 32 MU cg18241094 chr7 609545 32 N cg09702334 chr7 610337 32 MU cg02665399 chr7 611060 32 MU cg27495643 chr7 611397 32 MU cg01105362 chr7 1059627 32 MU cg10074347 chr7 2182153 32 MU cg13504346 chr7 2182182 32 N cg14397500 chr7 2214759 32 N cg18207099 chr7 2214790 32 MU cg04380800 chr7 100491337 32 MU cg08192683 chr7 157551275 32 MU cg17665357 chr8 2024249 32 MU cg23588952 chr8 2024300 32 N cg02618355 chr8 2024368 32 N cg24139520 chr8 28573832 32 MU cg14546712 chr8 41478669 32 MU cg13446043 chr9 4804648 32 MU cg07850533 chr9 4804779 32 N cg23087108 chr1 1120617 33 MU cg21285198 chr1 1360970 33 N cg00305774 chr1 1361077 33 MU cg22529900 chr1 8044553 33 MU cg12446447 chr1 8044581 33 N cg18861810 chr1 204985932 33 MU cg19707004 chr1 220271059 33 MU cg15393025 chr1 244318867 33 MU cg25466396 chr1 244319008 33 N cg10809560 chr10 79936959 33 MU cg15602037 chr10 79937002 33 N cg26109030 chr10 79937095 33 N cg03410667 chr10 81145959 33 N cg13500206 chr10 81146051 33 N cg25485956 chr10 81146099 33 MU cg19957119 chr10 102766322 33 N cg02009103 chr10 102766386 33 MU cg18360825 chr10 102766458 33 N cg09758896 chr10 131694575 33 N cg20800606 chr10 131694675 33 N cg26275858 chr10 131694689 33 MU cg12470014 chr10 133878016 33 MU cg00782697 chr10 134454481 33 MU cg22979615 chr11 1510644 33 MU cg26892308 chr11 1510724 33 N cg23822643 chr12 123341665 33 N cg04107639 chr12 123341681 33 MU cg24631609 chr12 124850781 33 MU cg10283674 chr12 124850836 33 N cg24336338 chr12 131452238 33 N cg03776878 chr12 131452292 33 N cg23617848 chr12 131452297 33 MU cg07964097 chr12 131708081 33 N cg23969380 chr12 131708174 33 MU cg19198993 chr12 131708300 33 N cg06004166 chr12 133102136 33 N cg12967384 chr12 133102272 33 MU cg09663836 chr12 133102331 33 N cg03571073 chr13 113464332 33 N cg02067585 chr13 113464357 33 MU cg19484164 chr13 113464412 33 N cg11724557 chr14 105965005 33 MU cg20384325 chr15 49268750 33 MU cg09077332 chr16 595794 33 N cg13113599 chr16 595848 33 N cg03614373 chr16 595904 33 MU cg02278319 chr16 596009 33 N cg08816443 chr16 926409 33 MU cg26619740 chr16 971556 33 N cg08214990 chr16 971579 33 MU cg02384338 chr16 19883969 33 MU cg04600253 chr16 50897243 33 N cg00900231 chr16 50897271 33 MU cg27088725 chr16 72459796 33 N cg07209034 chr16 72459825 33 MU cg02619656 chr16 72460083 33 MU cg05347108 chr16 72460114 33 N cg00940248 chr16 81929358 33 N cg03147430 chr16 81929395 33 MU cg05684569 chr16 81929465 33 MU cg04850875 chr16 85698376 33 MU cg26573923 chr16 87825195 33 MU cg05058069 chr16 88091975 33 MU cg08822215 chr16 89438651 33 N cg00432497 chr16 89438714 33 N cg01881136 chr16 89438758 33 MU cg09537792 chr16 89438892 33 N cg20931195 chr17 708009 33 MU cg08903077 chr17 4013000 33 MU cg20544516 chr17 17717183 33 N cg24161106 chr17 17717208 33 MU cg11393407 chr17 17717239 33 N cg09494646 chr17 17717252 33 N cg13245539 chr17 17717265 33 N cg07415388 chr17 17717276 33 N cg21564965 chr17 36626180 33 MU cg01443071 chr17 40120250 33 MU cg14376548 chr17 40120327 33 N cg27125505 chr17 43679177 33 MU cg04927033 chr17 43679265 33 N cg14110885 chr17 48911141 33 N cg13639451 chr17 48911157 33 MU cg13375248 chr17 48911181 33 N cg06852605 chr17 76422640 33 MU cg10251094 chr17 76454984 33 N cg05561386 chr17 76455102 33 MU cg20930294 chr17 76455269 33 MU cg27343663 chr17 79082811 33 MU cg04242349 chr17 79082840 33 MU cg09591867 chr17 79098558 33 MU cg11779900 chr17 80519722 33 MU cg16891318 chr18 72201590 33 N cg02840828 chr18 72201655 33 N cg05851240 chr18 72201663 33 N cg03148084 chr18 72201665 33 MU cg12811033 chr18 72201716 33 N cg03775901 chr18 72201764 33 N cg15441535 chr18 72201775 33 N cg00226849 chr18 72201785 33 N cg27172769 chr19 1255283 33 MU cg08176140 chr19 1255392 33 N cg24330746 chr19 7913785 33 MU cg14232365 chr19 7913869 33 N cg08337184 chr19 11558539 33 MU cg15804426 chr19 38993534 33 MU cg20469261 chr19 50361793 33 MU cg26570040 chr19 50361827 33 N cg23985239 chr19 50361910 33 N cg10393338 chr19 55798418 33 N cg22577385 chr19 55798477 33 MU cg13309510 chr2 3479637 33 MU cg19057227 chr2 3479762 33 N cg06886081 chr2 3479778 33 N cg09868768 chr2 70188605 33 N cg05298696 chr2 70188691 33 MU cg15365028 chr2 236442817 33 MU cg23551605 chr2 236442942 33 N cg27309098 chr2 236619748 33 MU cg14930335 chr2 236619767 33 MU cg21672829 chr2 236619825 33 N cg19759366 chr2 236715747 33 MU cg14165096 chr2 236715897 33 N cg07642035 chr2 236760940 33 N cg22373519 chr2 236761021 33 MU cg20351405 chr2 236761068 33 N cg01125214 chr2 236888769 33 MU cg07780694 chr2 238990408 33 MU cg18959169 chr2 240239988 33 N cg09549949 chr2 240240119 33 MU cg00397274 chr2 240240189 33 N cg27459437 chr20 37464594 33 MU cg19513282 chr20 62200711 33 N cg23957118 chr20 62200850 33 MU cg21520607 chr20 62200854 33 N cg04461566 chr22 22288352 33 N cg12894883 chr22 22288473 33 MU cg26285749 chr22 22288508 33 N cg00489795 chr22 24041104 33 MU cg17201947 chr3 42700007 33 N cg27351619 chr3 42700108 33 MU cg24169875 chr3 42700203 33 N cg06163701 chr3 51994615 33 MU cg21144338 chr3 51994712 33 N cg00071984 chr3 60617504 33 MU cg21856334 chr3 196754664 33 MU cg16185996 chr4 1008614 33 N cg07727358 chr4 1008738 33 MU cg12379954 chr4 144272609 33 MU cg12982893 chr4 144272716 33 N cg11599887 chr5 1552064 33 MU cg27039593 chr5 1552182 33 N cg25654517 chr6 29627131 33 N cg02589899 chr6 29627167 33 MU cg14656245 chr6 29627290 33 N cg25017994 chr6 29627296 33 N cg20637405 chr6 42046377 33 MU cg18116267 chr6 158418527 33 MU cg05987787 chr6 158448508 33 MU cg02272278 chr6 158448578 33 N cg12465250 chr6 163521043 33 MU cg05501666 chr6 163558674 33 N cg11724750 chr6 163558823 33 MU cg09883659 chr6 164520746 33 N cg25862072 chr6 164520845 33 MU cg22549545 chr6 166836358 33 MU cg08770734 chr6 166836393 33 N cg24873410 chr6 166836643 33 N cg26725047 chr6 166836730 33 N cg17292669 chr6 166836791 33 MU cg24692358 chr6 168550512 33 N cg16545749 chr6 168550568 33 MU cg09328444 chr6 169901471 33 N cg25823893 chr6 169901484 33 MU cg18714887 chr6 169901503 33 N cg27462820 chr7 636583 33 MU cg20392203 chr7 636590 33 MU cg02635482 chr7 672108 33 MU cg07266412 chr7 101723521 33 N cg17713751 chr7 101723568 33 MU cg06010390 chr7 101849120 33 MU cg19310653 chr7 101849184 33 N cg25484712 chr7 150828561 33 MU cg16419361 chr7 150828577 33 N cg15264162 chr7 154035993 33 MU cg19130610 chr7 156190025 33 MU cg17515932 chr7 156190074 33 N cg18686937 chr7 156190095 33 N cg20588703 chr7 156371613 33 MU cg02192555 chr8 9952926 33 MU cg26880445 chr8 9952965 33 N cg11824826 chr8 9953022 33 N cg24512544 chr8 141588943 33 MU cg17792192 chr9 138774526 33 MU cg14105458 chr9 139917436 33 MU cg10827460 chr1 879375 34 N cg15742605 chr1 879383 34 MU cg09756115 chr1 879415 34 N cg17469240 chr1 1190096 34 MU cg19081571 chr1 1190137 34 N cg13689260 chr1 1190238 34 N cg10623802 chr1 1913544 34 MU cg27442613 chr1 2801085 34 N cg03543952 chr1 2801150 34 MU cg02096887 chr1 10699831 34 MU cg02720091 chr1 10699958 34 N cg17836913 chr1 54723679 34 N cg24672056 chr1 54723730 34 MU cg08231577 chr1 117077088 34 MU cg02974976 chr1 117077219 34 N cg10245330 chr1 243652681 34 MU cg09879526 chr1 243652762 34 N cg11803859 chr10 125770124 34 N cg05342655 chr10 125770239 34 MU cg08398691 chr10 131217895 34 N cg07175255 chr10 131218009 34 MU cg20241059 chr10 133768242 34 MU cg05796331 chr10 133768294 34 N cg05480169 chr10 135067408 34 MU cg12587618 chr11 1979879 34 MU cg17676132 chr11 63886204 34 MU cg18093064 chr11 63886229 34 N cg01256440 chr11 63886459 34 MU cg06910673 chr11 63971624 34 N cg19352801 chr11 63971766 34 MU cg04815835 chr11 64522197 34 MU cg12071131 chr11 64522266 34 N cg27402782 chr11 65296794 34 MU cg09212369 chr11 77283117 34 MU cg11609668 chr11 117075078 34 MU cg07263694 chr12 111748299 34 N cg17125804 chr12 111748346 34 MU cg11550862 chr12 124856981 34 MU cg16277139 chr13 113744320 34 MU cg21561701 chr13 114193150 34 MU cg03479204 chr13 114193155 34 MU cg01515732 chr14 76012927 34 MU cg13738571 chr14 76012941 34 N cg14531093 chr14 102973717 34 MU cg27542341 chr15 75248086 34 MU cg06349762 chr16 888938 34 MU cg01125617 chr16 889011 34 N cg07205796 chr16 1187722 34 MU cg02261541 chr16 4050315 34 MU cg06171406 chr16 4050400 34 N cg06480482 chr16 29265720 34 MU cg26796743 chr16 85547467 34 MU cg26953293 chr16 85684108 34 MU cg03187934 chr16 90166344 34 MU cg20899146 chr17 1897348 34 MU cg10709026 chr17 3716247 34 MU cg02317397 chr17 3716279 34 N cg25382652 chr17 3716426 34 MU cg04248364 chr17 3716558 34 N cg21289738 chr17 8383742 34 N cg21871213 chr17 8383800 34 MU cg25495024 chr17 9189607 34 MU cg27198013 chr17 17714293 34 MU cg09015921 chr17 17714347 34 N cg22946219 chr17 17714414 34 N cg17876831 chr17 18918410 34 MU cg22671072 chr17 18918466 34 N cg24209115 chr17 18918472 34 N cg23665668 chr17 38245507 34 MU cg23526824 chr17 38245542 34 N cg10858898 chr17 38245607 34 N cg20404150 chr17 40705273 34 MU cg00439089 chr17 48263150 34 N cg16514513 chr17 48263159 34 N cg11615029 chr17 48263189 34 MU cg11993636 chr17 48263198 34 N cg25735490 chr17 48263204 34 MU cg14193007 chr17 77953016 34 MU cg04194055 chr17 77953088 34 MU cg17602102 chr17 78796159 34 MU cg02910299 chr17 78796166 34 N cg02775417 chr19 643457 34 MU cg12062672 chr19 643555 34 N cg19222405 chr19 1826867 34 MU cg03924776 chr19 1826924 34 N cg04584009 chr19 1826957 34 N cg23429986 chr19 3382104 34 N cg22731204 chr19 3382127 34 MU cg21740826 chr19 4037881 34 MU cg26501450 chr19 4038221 34 MU cg22268467 chr19 4361713 34 MU cg24419436 chr19 4446031 34 MU cg09377855 chr19 4446336 34 MU cg05421550 chr19 4446485 34 N cg02305651 chr19 5997013 34 N cg07572348 chr19 5997117 34 MU cg17702388 chr19 16466480 34 N cg04738220 chr19 16466617 34 MU cg11013793 chr19 16466636 34 N cg00029640 chr19 35770141 34 N cg08113595 chr19 35770164 34 MU cg07918620 chr19 35800925 34 MU cg15761414 chr19 35801014 34 N cg22923523 chr19 47549464 34 MU cg22182287 chr2 3452347 34 N cg00548824 chr2 3452407 34 MU cg17738010 chr2 3452437 34 N cg06360976 chr2 3452543 34 N cg08182975 chr2 7164527 34 MU cg20995753 chr2 7164586 34 N cg23079012 chr2 8343710 34 MU cg23393100 chr2 25457204 34 N cg13828701 chr2 25457215 34 MU cg26160153 chr2 27373014 34 N cg24874524 chr2 27373088 34 MU cg14949065 chr2 27373128 34 MU cg02930665 chr2 27373213 34 N cg25114693 chr2 105695949 34 MU cg16326123 chr2 240015886 34 MU cg09669931 chr2 240058920 34 MU cg18359321 chr2 240058950 34 MU cg19426572 chr20 61919450 34 MU cg14058699 chr21 47421177 34 N cg24310904 chr21 47421270 34 N cg21308336 chr21 47421319 34 MU cg13082654 chr21 47811350 34 MU cg26017564 chr3 172034780 34 MU cg03999583 chr3 197385493 34 MU cg24715682 chr3 197877792 34 MU cg05145898 chr4 6883799 34 N cg07976816 chr4 6883843 34 MU cg14853946 chr4 6883863 34 N cg01592387 chr4 7774382 34 N cg20929387 chr4 7774510 34 MU cg11565007 chr4 106978683 34 MU cg26402435 chr4 190661492 34 N cg05970547 chr4 190661634 34 MU cg13346820 chr5 865318 34 N cg11447335 chr5 865350 34 MU cg02493524 chr5 3768103 34 N cg19541998 chr5 3768144 34 N cg23988801 chr5 3768181 34 MU cg09429437 chr5 6755265 34 N cg15145174 chr5 6755386 34 MU cg26964629 chr5 6755439 34 N cg08970011 chr6 3293371 34 MU cg23927832 chr6 30633356 34 MU cg15055187 chr6 30633386 34 N cg19965099 chr6 31801683 34 MU cg19816983 chr6 31937343 34 N cg10674805 chr6 31937437 34 MU cg05909390 chr6 31937454 34 N cg20480259 chr6 31937462 34 N cg09230350 chr6 31937582 34 N cg09299390 chr7 1866243 34 MU cg13037895 chr7 2262292 34 N cg16573836 chr7 2262355 34 MU cg24941703 chr7 2262479 34 N cg02527886 chr7 2968316 34 MU cg10939949 chr7 2968406 34 N cg06746318 chr7 101755186 34 MU cg19410378 chr7 152551811 34 MU cg04058237 chr7 156337107 34 N cg21839504 chr7 156337168 34 MU cg18195333 chr7 156337209 34 N cg19237063 chr7 157666168 34 MU cg16142313 chr8 28950249 34 N cg07237653 chr8 28950303 34 MU cg25987871 chr8 141231538 34 MU cg14487379 chr9 135481870 34 MU cg14008164 chr9 141105202 34 MU

TABLE 3 500 CpGs iteratively added to distinguish between similar pairs of tissues/cell types Illumina Probe Tissue/Cell % Tissue/Cell % Name Chr. Position Type 1 Meth. Type 2 Meth. 1 cg01156249 16 4714794 Monocytes 3.2% B-cells 90.9% 2 cg25574765 11 70211531 NK-cells 0.0% Breast 91.5% 3 cg08960448 16 4838658 Neurons 99.0% Panc duct cells 1.7% 4 cg14088921 8 98944936 Panc beta cells 83.8% Panc duct cells 2.2% 5 cg26872907 6 161796854 Monocytes 9.3% Neutrophils 94.8% 6 cg12213680 6 24877462 Eosinophils 15.9% Neutrophils 78.4% 7 cg13910785 6 32549849 Colon 42.8% Rectum 4.6% 8 cg06768361 12 117480333 B-cells 96.8% Eryth Prog 1.4% 9 cg00660167 17 80200974 CD4+ T-cells 96.6% Neutrophils 8.2% 10 cg22505006 1 154981829 CD4+ T-cells 9.7% CD8+ T-cells 82.8% 11 cg02380585 17 33776683 NK-cells 28.6% CD8+ T-cells 80.5% 12 cg18121066 17 80794274 Breast 31.4% Uterus 86.3% 13 cg26686732 20 22562737 Colon 38.3% Rectum 6.1% 14 cg19384241 2 55393977 Cervix 20.0% Uterus 67.1% 15 cg23905789 6 32549935 Adipocytes 12.7% Bladder 84.8% 16 cg06125903 20 57583328 B-cells 3.8% NK-cells 85.0% 17 cg12911732 1 50744322 Breast 9.2% Head and neck 87.2% 18 cg13763287 9 97856079 Colon 64.1% Rectum 95.7% 19 cg24011073 2 11917787 NK-cells 37.4% CD8+ T-cells 86.8% 20 cg20504791 20 22562478 Colon 43.1% Rectum 11.8% 21 cg16953816 12 123349952 Kidney 26.7% Uterus 92.3% 22 cg17480035 6 32552670 Cervix 83.5% Uterus 38.1% 23 cg17830515 7 1151900 Vasc endo cells 17.2% Bladder 81.0% 24 cg07984133 13 28544592 Colon 22.7% Rectum 53.7% 25 cg21498547 8 1651128 Esophagus 35.7% Stomach 85.1% 26 cg04168675 2 225809655 NK-cells 76.8% CD8+ T-cells 27.8% 27 cg04650948 17 46798298 Colon 62.1% Rectum 31.2% 28 cg08691577 5 14668489 CD4+ T-cells 76.1% CD8+ T-cells 15.4% 29 cg20240243 15 100154002 Eosinophils 19.9% Neutrophils 82.0% 30 cg08061524 22 46472568 Bladder 69.4% Uterus 14.9% 31 cg24051749 1 39340282 Colon 52.3% Rectum 21.5% 32 cg24888257 6 31785597 Breast 7.5% Uterus 61.7% 33 cg24586205 1 222162028 Cervix 4.1% Uterus 44.7% 34 cg22210145 13 28545263 Colon 25.8% Rectum 56.5% 35 cg13563298 9 95948059 Bladder 88.6% Colon 18.0% 36 cg14011789 17 75452044 NK-cells 76.7% CD8+ T-cells 27.8% 37 cg10244976 16 1014818 Breast 87.9% Prostate 17.6% 38 cg14825735 6 1594066 Colon 29.1% Rectum 59.6% 39 cg22933800 6 32605704 Cervix 82.3% Uterus 43.9% 40 cg27091865 2 3826610 Kidney 22.4% Uterus 88.0% 41 cg04855216 5 118609467 Panc beta cells 85.5% Panc acinar cells 5.0% 42 cg06665913 11 113947036 Eosinophils 22.2% Neutrophils 83.4% 43 cg06935361 13 32964454 CD4+ T-cells 81.0% CD8+ T-cells 21.1% 44 cg03635532 19 40376883 Colon 84.0% Rectum 53.9% 45 cg24441810 2 120436039 NK-cells 59.0% CD8+ T-cells 10.5% 46 cg00103771 6 32525805 Cervix 81.4% Uterus 46.0% 47 cg12374721 17 46799640 Colon 38.9% Rectum 9.1% 48 cg02159489 17 79459563 Esophagus 88.0% Stomach 39.2% 49 cg11128983 5 175297531 Colon 70.8% Rectum 41.1% 50 cg18173726 15 95713100 Cervix 31.4% Uterus 65.9% 51 cg17311865 14 22975521 NK-cells 63.4% CD8+ T-cells 15.0% 52 cg05222982 13 28545214 Colon 31.8% Rectum 60.8% 53 cg08467103 2 65593933 Cervix 10.5% Uterus 44.6% 54 cg16871561 12 98986887 CD4+ T-cells 15.7% CD8+ T-cells 74.1% 55 cg18833880 10 131650741 Colon 60.9% Rectum 89.4% 56 cg18236982 1 236065291 Cervix 46.8% Uterus 80.0% 57 cg18236877 1 10733511 Colon 63.9% Rectum 35.4% 58 cg13668207 12 65904452 Bladder 73.1% Uterus 20.4% 59 cg15564619 1 2163437 NK-cells 82.0% CD8+ T-cells 34.4% 60 cg16514085 6 32552152 Colon 22.2% Rectum 50.5% 61 cg05078091 11 129984345 Eosinophils 21.0% Neutrophils 81.2% 62 cg18001714 5 14871894 Esophagus 4.7% Stomach 51.0% 63 cg27659049 12 115173713 Colon 59.6% Rectum 31.8% 64 cg05509190 2 177503639 Cervix 24.0% Uterus 56.4% 65 cg02582848 1 224075899 Breast 77.1% Uterus 23.7% 66 cg01263942 10 695859 Esophagus 19.0% Head and neck 79.6% 67 cg05222995 12 115124973 Colon 50.8% Rectum 23.2% 68 cg13453904 10 133484610 Bladder 7.8% Esophagus 66.6% 69 cg17113883 6 128239611 NK-cells 80.6% CD8+ T-cells 33.0% 70 cg01774894 17 46674395 Cervix 80.2% Uterus 48.1% 71 cg21596498 19 42618407 B-cells 11.2% NK-cells 91.1% 72 cg25045219 17 263096 CD4+ T-cells 10.3% CD8+ T-cells 67.0% 73 cg15424233 2 9865477 Colon 52.2% Rectum 79.8% 74 cg07077965 2 177503592 Cervix 26.2% Uterus 58.3% 75 cg19602081 4 111553161 Colon 39.2% Rectum 12.6% 76 cg26103369 14 75153307 Eosinophils 20.1% Neutrophils 80.2% 77 cg19256731 20 1874527 Colon 67.3% Rectum 40.8% 78 cg19626138 8 37731969 NK-cells 19.1% CD8+ T-cells 66.6% 79 cg25006077 3 152176018 Cervix 41.8% Uterus 73.8% 80 cg25958911 8 145495665 Esophagus 72.6% Stomach 27.4% 81 cg06774703 17 39647583 Colon 73.5% Rectum 47.0% 82 cg01543583 14 59947673 Colon 53.0% Rectum 79.3% 83 cg19543867 7 112124979 Cervix 22.4% Uterus 54.4% 84 cg04329870 2 87048747 CD4+ T-cells 84.1% CD8+ T-cells 29.9% 85 cg03943773 4 111552308 Colon 33.7% Rectum 7.6% 86 cg05995465 2 240313602 Cervix 44.0% Uterus 75.9% 87 cg05263838 2 227659406 Colon 64.5% Rectum 38.5% 88 cg19351954 7 156259207 Breast 75.2% Head and neck 16.2% 89 cg05849676 12 10171151 Bladder 29.3% Uterus 80.9% 90 cg17920646 8 216578 Colon 60.7% Rectum 35.0% 91 cg04778178 7 27162294 Bladder 89.8% Esophagus 32.1% 92 cg18348836 1 60282400 Eosinophils 19.1% Neutrophils 78.3% 93 cg10708271 3 112692897 NK-cells 57.9% CD8+ T-cells 10.4% 94 cg24938632 17 46830125 Colon 28.4% Rectum 53.8% 95 cg02123534 7 965485 CD4+ T-cells 70.2% CD8+ T-cells 17.5% 96 cg08042322 12 104233595 Colon 73.9% Rectum 48.6% 97 cg05797224 1 94277532 Cervix 37.0% Uterus 68.7% 98 cg16966496 12 115172598 Colon 64.1% Rectum 38.8% 99 cg22676075 6 135203613 Esophagus 30.2% Stomach 75.2% 100 cg14565903 19 14260591 Kidney 74.9% Uterus 14.2% 101 cg21229570 12 115124768 Colon 40.2% Rectum 15.0% 102 cg23244761 6 161796850 Monocytes 13.9% Neutrophils 96.9% 103 cg24527636 1 27849295 NK-cells 70.6% CD8+ T-cells 23.2% 104 cg27169860 2 66218712 Cervix 18.7% Uterus 50.3% 105 cg10606698 6 76203642 Colon 68.6% Rectum 43.4% 106 cg04696274 20 43977531 Cervix 36.9% Uterus 68.1% 107 cg21175246 14 95964097 Colon 65.6% Rectum 40.4% 108 cg01782486 1 154986512 CD4+ T-cells 34.6% CD8+ T-cells 87.1% 109 cg18992570 5 134662888 Colon 83.7% Rectum 58.6% 110 cg05528899 17 57120 Cervix 36.8% Uterus 67.7% 111 cg07930673 2 2650283 NK-cells 64.0% CD8+ T-cells 17.2% 112 cg22022580 19 37266605 Colon 75.3% Rectum 50.2% 113 cg00584840 3 112903054 Esophagus 21.0% Head and neck 80.2% 114 cg18349241 5 89772071 Eosinophils 21.5% Neutrophils 80.0% 115 cg02220965 16 31128310 Colon 29.1% Rectum 54.2% 116 cg22645201 7 120701829 Cervix 42.6% Uterus 73.4% 117 cg25225073 14 90528983 Esophagus 10.4% Stomach 55.4% 118 cg24925400 4 111552353 Colon 40.2% Rectum 15.1% 119 cg06579465 17 30152101 Cervix 36.4% Uterus 67.2% 120 cg26681770 20 56247302 NK-cells 24.5% CD8+ T-cells 70.7% 121 cg04528477 2 176932458 Colon 22.3% Rectum 47.3% 122 cg03634833 7 965534 CD4+ T-cells 67.6% CD8+ T-cells 15.5% 123 cg11323985 8 18541497 Breast 70.2% Uterus 17.6% 124 cg14202783 9 130662561 Colon 45.9% Rectum 70.8% 125 cg01405040 2 176964137 Cervix 57.4% Uterus 26.7% 126 cg22198853 6 1594411 Colon 41.5% Rectum 66.4% 127 cg08879910 6 29974319 Esophagus 5.4% Stomach 49.8% 128 cg04011338 2 238777587 Cervix 35.0% Uterus 65.6% 129 cg23336481 6 32634344 Colon 21.9% Rectum 46.7% 130 cg08223760 1 43291209 Eosinophils 21.5% Neutrophils 79.9% 131 cg00263248 14 98444151 NK-cells 64.8% CD8+ T-cells 18.9% 132 cg03686593 18 11149470 Colon 40.4% Rectum 15.7% 133 cg17185710 3 151984822 Cervix 28.0% Uterus 58.6% 134 cg05613718 7 38355100 Monocytes 82.8% Neutrophils 7.2% 135 cg04497820 5 158086454 Colon 56.0% Rectum 31.4% 136 cg20676788 13 49379791 Cervix 33.9% Uterus 64.4% 137 cg14989243 6 76203530 Colon 62.3% Rectum 37.7% 138 cg10581012 11 110384827 Kidney 83.9% Uterus 24.7% 139 cg23685155 12 54396440 Colon 44.9% Rectum 20.3% 140 cg23264547 11 44257668 Cervix 33.4% Uterus 63.7% 141 cg13120534 14 50063923 Colon 52.0% Rectum 76.5% 142 cg18637238 12 10543073 CD4+ T-cells 86.5% CD8+ T-cells 36.2% 143 cg09885622 5 1504991 NK-cells 16.0% CD8+ T-cells 61.7% 144 cg03455736 22 49882497 Cervix 34.7% Uterus 65.0% 145 cg20518446 11 62315034 Colon 66.7% Rectum 42.2% 146 cg10888111 2 1637068 Colon 64.3% Rectum 88.8% 147 cg18673341 7 22481962 Cervix 64.7% Uterus 34.5% 148 cg26107890 3 124860686 Bladder 5.1% Esophagus 57.3% 149 cg18450582 7 95546539 Esophagus 30.7% Head and neck 86.7% 150 cg23478225 2 46113875 Colon 73.5% Rectum 49.0% 151 cg08077807 14 62001072 Eosinophils 20.3% Neutrophils 78.5% 152 cg02196256 7 148321082 NK-cells 29.8% CD8+ T-cells 75.2% 153 cg16053651 6 168857803 Colon 55.1% Rectum 79.6% 154 cg23238315 7 156259194 Breast 84.8% Head and neck 27.2% 155 cg11777506 2 114880656 Colon 50.4% Rectum 74.9% 156 cg26986871 12 10545083 CD4+ T-cells 57.2% CD8+ T-cells 7.6% 157 cg23936031 7 27183133 Cervix 61.6% Uterus 31.5% 158 cg00555456 7 96745696 Colon 42.4% Rectum 17.9% 159 cg03339817 2 128458281 Monocytes 22.1% Neutrophils 97.0% 160 cg15857470 7 104945248 Colon 39.0% Rectum 63.4% 161 cg03260021 12 128170477 Cervix 37.1% Uterus 67.2% 162 cg16596052 10 121271746 NK-cells 91.0% CD8+ T-cells 46.3% 163 cg05803631 1 7844446 Esophagus 3.4% Stomach 47.4% 164 cg10443315 8 7947282 Colon 67.4% Rectum 43.3% 165 cg27592331 18 19756582 Adipocytes 79.8% Vasc endo cells 8.4% 166 cg24730157 1 984428 Colon 43.6% Rectum 67.5% 167 cg09696385 7 127528039 Eosinophils 22.7% Neutrophils 80.7% 168 cg08329754 17 78797015 Cervix 40.1% Uterus 70.1% 169 cg09728393 6 17851756 Colon 65.7% Rectum 41.9% 170 cg16810279 17 76254129 CD4+ T-cells 13.8% CD8+ T-cells 63.2% 171 cg12589143 6 29894341 Colon 34.2% Rectum 10.5% 172 cg06518838 6 170403264 Esophagus 73.3% Stomach 31.0% 173 cg12465678 1 27953336 NK-cells 13.8% CD8+ T-cells 58.5% 174 cg25503695 4 798983 Colon 57.1% Rectum 80.7% 175 cg15864691 7 27217606 Cervix 29.9% Uterus 59.8% 176 cg01951086 4 111551642 Colon 41.5% Rectum 17.9% 177 cg20485607 1 120217696 Cervix 77.8% Uterus 47.9% 178 cg10041379 6 33221464 Colon 82.6% Rectum 59.1% 179 cg09893465 10 134588040 Esophagus 40.7% Head and neck 96.5% 180 cg13058457 15 38422160 Kidney 67.7% Uterus 9.4% 181 cg16786640 4 3485263 Colon 31.7% Rectum 55.1% 182 cg04365699 3 177320997 Eosinophils 18.6% Neutrophils 76.4% 183 cg02561103 7 115862891 Cervix 47.9% Uterus 77.7% 184 cg14255824 9 71795999 Vasc endo cells 16.1% Uterus 81.4% 185 cg20156237 8 144543750 NK-cells 76.4% CD8+ T-cells 32.2% 186 cg06830784 2 183731824 Colon 43.9% Rectum 20.5% 187 cg13600149 9 98079129 CD4+ T-cells 7.3% CD8+ T-cells 56.5% 188 cg05359518 15 57508437 Colon 76.3% Rectum 52.9% 189 cg11846905 12 86342281 Cervix 36.5% Uterus 66.2% 190 cg24314564 3 188668940 Monocytes 12.7% Neutrophils 86.9% 191 cg07227024 2 202163482 Esophagus 28.2% Stomach 70.0% 192 cg00040312 12 54424679 Colon 44.8% Rectum 21.6% 193 cg06430465 16 1014935 Colon 54.8% Rectum 31.7% 194 cg26923863 4 1221838 NK-cells 38.1% CD8+ T-cells 82.1% 195 cg09001356 17 78797061 Cervix 45.2% Uterus 74.6% 196 cg19236493 17 46629596 Breast 86.6% Head and neck 30.8% 197 cg06434344 16 3515991 Colon 60.0% Rectum 83.1% 198 cg24345747 2 87015813 CD4+ T-cells 66.4% CD8+ T-cells 17.7% 199 cg05877497 2 66667946 Colon 25.9% Rectum 48.8% 200 cg27224986 4 53906430 Eosinophils 21.9% Neutrophils 79.6% 201 cg12502325 7 111901177 Cervix 46.5% Uterus 75.9% 202 cg24538625 11 51579523 Colon 61.3% Rectum 38.4% 203 cg25485875 10 134600998 Esophagus 10.1% Stomach 51.5% 204 cg15986668 1 41156730 Colon 40.0% Rectum 62.9% 205 cg27243507 3 101808873 Cervix 40.7% Uterus 70.0% 206 cg12754495 3 151102703 Adipocytes 12.9% Uterus 84.2% 207 cg08704934 3 194826585 Colon 70.1% Rectum 47.3% 208 cg09232358 14 23015657 NK-cells 62.0% CD8+ T-cells 18.0% 209 cg10065209 1 1267559 Colon 68.5% Rectum 91.3% 210 cg03318654 2 87034200 CD4+ T-cells 74.5% CD8+ T-cells 26.2% 211 cg03653399 8 142233436 Bladder 82.1% Uterus 31.1% 212 cg20202760 8 53499558 Cervix 44.3% Uterus 73.6% 213 cg11146821 1 62664360 Colon 50.4% Rectum 73.2% 214 cg02827046 11 125649030 Colon 75.5% Rectum 52.7% 215 cg02695349 7 38269241 Cervix 31.9% Uterus 61.1% 216 cg05390968 16 86598792 Colon 59.3% Rectum 36.9% 217 cg07355841 16 67427339 Esophagus 6.8% Stomach 48.0% 218 cg06706813 3 194826411 Colon 76.2% Rectum 53.8% 219 cg07795928 1 226900896 Eosinophils 17.9% Neutrophils 75.3% 220 cg03810198 17 74679597 Cervix 56.8% Uterus 27.6% 221 cg04416414 19 14260587 Kidney 73.1% Uterus 14.9% 222 cg01733176 4 111561070 Colon 37.1% Rectum 14.7% 223 cg02902412 18 77087900 CD4+ T-cells 91.6% CD8+ T-cells 43.9% 224 cg00826640 7 157381078 Colon 50.4% Rectum 72.7% 225 cg10092377 1 200880981 Cervix 78.3% Uterus 49.2% 226 cg1l753157 14 99734018 NK-cells 62.0% CD8+ T-cells 18.0% 227 cg23651889 3 188115336 Colon 39.1% Rectum 61.3% 228 cg10293282 16 50977896 Cervix 46.9% Uterus 76.0% 229 cg04689773 14 92199710 Colon 61.5% Rectum 39.3% 230 cg25953239 2 176982342 Vasc endo cells 3.4% Bladder 64.1% 231 cg14676272 8 49836160 Breast 90.3% Head and neck 35.7% 232 cg04131969 2 33951647 Cervix 76.5% Uterus 47.5% 233 cg06449334 20 17593441 CD4+ T-cells 63.0% CD8+ T-cells 15.7% 234 cg13292042 4 111535334 Colon 40.5% Rectum 18.3% 235 cg00804179 3 13083974 Breast 95.1% Uterus 42.6% 236 cg12377972 5 132009630 Eosinophils 21.4% Neutrophils 78.6% 237 cg23443098 10 13987636 Cervix 41.5% Uterus 70.4% 238 cg27111890 21 43823749 NK-cells 61.0% CD8+ T-cells 17.3% 239 cg23052826 2 232479675 Colon 17.0% Rectum 39.0% 240 cg01102073 3 128204813 Colon 35.3% Rectum 57.3% 241 cg00375132 10 22768370 Cervix 40.4% Uterus 69.3% 242 cg23697546 12 54424085 Colon 30.9% Rectum 8.9% 243 cg27499850 16 73127462 Colon 45.5% Rectum 67.4% 244 cg24037651 8 143449800 Monocytes 14.4% Neutrophils 88.5% 245 cg12583572 7 1967976 Colon 72.4% Rectum 50.6% 246 cg13468685 2 68592737 NK-cells 12.3% CD8+ T-cells 55.8% 247 cg22772386 6 167763320 Cervix 47.9% Uterus 76.6% 248 cg05609780 3 24001171 Colon 70.4% Rectum 48.6% 249 cg14477767 4 170195438 CD4+ T-cells 26.5% CD8+ T-cells 73.7% 250 cg20075156 4 11430717 Esophagus 1.7% Stomach 42.9% 251 cg26937434 16 21245151 Breast 36.3% Head and neck 86.4% 252 cg14269813 9 3899055 Colon 37.0% Rectum 58.8% 253 cg09636715 7 27217057 Cervix 27.5% Uterus 56.2% 254 cg26708319 4 111552040 Colon 32.1% Rectum 10.4% 255 cg07163168 1 76899846 Colon 72.2% Rectum 50.5% 256 cg11130630 12 109899089 NK-cells 18.1% CD8+ T-cells 61.5% 257 cg13488570 1 2222253 Kidney 63.6% Uterus 5.6% 258 cg18380211 5 98247404 Eosinophils 18.7% Neutrophils 75.8% 259 cg27424906 1 62662166 Colon 61.4% Rectum 83.1% 260 cg07232063 1 86953050 Cervix 36.8% Uterus 65.5% 261 cg13353717 6 32634276 Colon 15.1% Rectum 36.8% 262 cg25967170 12 67127781 Cervix 34.6% Uterus 63.2% 263 cg04846710 1 145478286 Colon 39.1% Rectum 17.4% 264 cg18857618 2 87048489 CD4+ T-cells 81.4% CD8+ T-cells 34.5% 265 cg04615460 12 115125098 Colon 49.6% Rectum 28.0% 266 cg08061598 6 112081538 Cervix 22.5% Uterus 51.1% 267 cg13751386 9 97856033 Colon 60.6% Rectum 82.2% 268 cg24506221 1 110230401 Esophagus 53.2% Stomach 12.0% 269 cg01802772 1 55014160 Colon 30.5% Rectum 8.9% 270 cg07769588 19 10655622 Colon 45.0% Rectum 66.5% 271 cg11945507 8 142233382 Bladder 73.1% Uterus 23.4% 272 cg19579080 17 66588601 Cervix 39.3% Uterus 67.9% 273 cg19301114 6 161796785 Monocytes 10.4% Neutrophils 84.3% 274 cg26827987 16 86232457 Breast 3.9% Head and neck 53.1% 275 cg23705108 4 27102875 Colon 63.5% Rectum 42.0% 276 cg12910830 5 162929657 Eosinophils 20.9% Neutrophils 77.9% 277 cg18303397 3 129160135 CD4+ T-cells 24.0% CD8+ T-cells 70.4% 278 cg23580725 14 99707721 NK-cells 65.9% CD8+ T-cells 22.7% 279 cg12569246 6 76203666 Colon 64.2% Rectum 42.7% 280 cg25294526 17 7786177 Cervix 43.0% Uterus 71.4% 281 cg17030488 12 115172748 Colon 80.6% Rectum 59.2% 282 cg19304088 4 111547790 Colon 39.9% Rectum 18.6% 283 cg09884146 2 65593908 Cervix 19.0% Uterus 47.4% 284 cg10617784 2 71134586 Colon 24.3% Rectum 45.6% 285 cg23027574 6 30043194 Esophagus 4.8% Stomach 45.7% 286 cg25020969 7 1967802 Colon 81.1% Rectum 59.9% 287 cg01806427 5 123737813 NK-cells 29.5% CD8+ T-cells 72.7% 288 cg14727763 10 78961643 Cervix 41.8% Uterus 70.2% 289 cg21091766 5 176898673 CD4+ T-cells 85.0% CD8+ T-cells 38.7% 290 cg05218882 3 132440131 Colon 59.8% Rectum 81.0% 291 cg01189072 1 1610660 Bladder 87.8% Esophagus 35.7% 292 cg12626036 5 134662980 Colon 77.6% Rectum 56.5% 293 cg20701689 2 183731832 Colon 40.5% Rectum 19.4% 294 cg14170999 17 79380515 Cervix 25.5% Uterus 53.8% 295 cg01055099 16 3516099 Colon 41.8% Rectum 62.9% 296 cg00779602 3 66002927 Breast 87.3% Head and neck 38.4% 297 cg18254848 19 40227949 Eosinophils 23.7% Neutrophils 80.6% 298 cg014O3655 13 43702940 Cervix 47.2% Uterus 75.5% 299 cg07058086 8 29120186 Colon 15.4% Rectum 36.4% 300 cg14338425 8 120220799 Esophagus 9.2% Stomach 50.1% 301 cg22911650 19 51645423 NK-cells 27.8% CD8+ T-cells 70.8% 302 cg08890690 4 111553018 Colon 31.0% Rectum 10.0% 303 cg18174654 2 87019612 CD4+ T-cells 81.0% CD8+ T-cells 34.9% 304 cg07284231 8 700689 Bladder 79.2% Uterus 29.6% 305 cg05668703 17 79380493 Cervix 26.8% Uterus 55.0% 306 cg22244192 6 33943837 Colon 50.0% Rectum 29.1% 307 cg17144149 17 46656572 Colon 47.5% Rectum 68.4% 308 cg18179075 7 129913008 Colon 52.6% Rectum 31.7% 309 cg08O13737 17 35480754 Cervix 39.1% Uterus 67.3% 310 cg24820809 22 36019252 Colon 65.8% Rectum 86.7% 311 cg01898661 16 90114063 Esophagus 8.8% Stomach 49.6% 312 cg12279419 6 33943662 Colon 49.3% Rectum 28.4% 313 cg05154454 9 113100053 Cervix 40.5% Uterus 68.6% 314 cg09654116 7 158816213 CD4+ T-cells 87.0% CD8+ T-cells 41.4% 315 cg01757124 17 76320916 Colon 39.9% Rectum 19.1% 316 cg21261709 7 104887042 Eosinophils 21.4% Neutrophils 78.0% 317 cg00910015 2 3826621 Kidney 35.2% Uterus 92.9% 318 cg11551216 12 115125619 Colon 48.7% Rectum 28.0% 319 cg03787603 7 101768610 Cervix 40.1% Uterus 68.2% 320 cg14987787 12 115105220 Colon 38.1% Rectum 17.4% 321 cg00031277 4 101943443 Cervix 42.7% Uterus 70.8% 322 cg10752315 3 150238463 Colon 68.8% Rectum 89.5% 323 cg03687070 2 16210108 Bladder 91.2% Esophagus 39.1% 324 cg23597015 15 68932319 Bladder 81.1% Uterus 31.6% 325 cg17945429 1 158174027 NK-cells 60.4% CD8+ T-cells 17.5% 326 cg03O51836 20 22563319 Colon 53.9% Rectum 33.2% 327 cg26146569 15 31637592 CD4+ T-cells 60.6% CD8+ T-cells 15.3% 328 cg05552183 6 42928497 Esophagus 1.6% Stomach 42.2% 329 cg09273483 1 244511482 Cervix 22.4% Uterus 50.5% 330 cg00968270 16 87471788 Colon 71.5% Rectum 50.8% 331 cg11354682 19 10978833 Monocytes 11.2% Neutrophils 84.9% 332 cg17965712 5 2757802 Breast 68.8% Uterus 16.6% 333 cg02959678 19 1047604 Colon 52.6% Rectum 73.3% 334 cg13600632 9 129169002 Cervix 44.9% Uterus 73.0% 335 cg08822897 11 64258103 Colon 51.5% Rectum 72.1% 336 cg12192145 14 55849528 Eosinophils 20.6% Neutrophils 77.1% 337 cg08707875 1 224575077 Colon 68.6% Rectum 48.0% 338 cg12608692 5 16742020 Cervix 33.5% Uterus 61.5% 339 cg13818708 6 30291314 Colon 86.4% Rectum 65.8% 340 cg06419846 11 66083697 CD4+ T-cells 94.8% CD8+ T-cells 49.6% 341 cg10682299 4 111550154 Colon 48.4% Rectum 27.8% 342 cg02042623 12 104227548 Cervix 44.1% Uterus 72.1% 343 cg15912390 3 194826514 Colon 75.1% Rectum 54.5% 344 cg13673837 19 15306670 Esophagus 3.0% Stomach 43.6% 345 cg13016773 12 72081013 Colon 45.5% Rectum 66.1% 346 cg05355436 10 63520873 Cervix 36.3% Uterus 64.2% 347 cg02866149 20 35405989 Colon 59.9% Rectum 39.3% 348 cg18760534 2 2964910 Bladder 91.9% Uterus 43.7% 349 cg03117976 18 11149435 Colon 32.8% Rectum 12.3% 350 cg02225720 17 3623472 Cervix 17.3% Uterus 45.1% 351 cg24798398 2 176937524 Colon 18.9% Rectum 39.4% 352 cg00842595 17 263193 CD4+ T-cells 34.2% CD8+ T-cells 79.4% 353 cg15899743 14 22985112 NK-cells 69.4% CD8+ T-cells 26.6% 354 cg12744859 17 46669492 Breast 75.8% Head and neck 27.1% 355 cg00723994 12 115105290 Colon 44.3% Rectum 23.9% 356 cg16139202 5 134662788 Colon 88.0% Rectum 67.6% 357 cg26975184 7 140267040 Esophagus 42.1% Head and neck 96.8% 358 cg17470397 3 193308169 Eosinophils 20.8% Neutrophils 77.2% 359 cg03144922 4 111540992 Colon 34.2% Rectum 13.9% 360 cg01401824 2 159902017 Cervix 47.6% Uterus 75.4% 361 cg06796779 3 128204927 Colon 43.2% Rectum 63.5% 362 cg00892792 8 142297584 Colon 79.0% Rectum 58.7% 363 cg14054883 9 93619467 NK-cells 58.7% CD8+ T-cells 16.2% 364 cg05617307 10 121413182 CD4+ T-cells 33.0% CD8+ T-cells 78.2% 365 cg07249765 7 4244643 Cervix 93.0% Uterus 65.3% 366 cg21150839 20 62209634 Colon 57.5% Rectum 77.8% 367 cg26992028 7 1030287 Colon 70.6% Rectum 50.3% 368 cg07702235 2 71983542 Cervix 20.1% Uterus 47.8% 369 cg20559385 6 106049591 Monocytes 11.6% Neutrophils 84.2% 370 cg23207527 6 17283113 Colon 61.2% Rectum 40.9% 371 cg22355889 11 107461585 Esophagus 12.9% Stomach 53.4% 372 cg22486928 12 96251729 Colon 63.7% Rectum 43.4% 373 cg19019345 11 8888814 Cervix 20.2% Uterus 47.9% 374 cg10l11816 16 22384715 NK-cells 54.9% CD8+ T-cells 12.5% 375 cg14783123 8 68022262 Eosinophils 23.3% Neutrophils 79.6% 376 cg24896021 8 6630930 Colon 73.1% Rectum 52.9% 377 cg02058628 1 2164542 CD4+ T-cells 38.4% CD8+ T-cells 83.4% 378 cg02208793 11 84634615 Colon 68.2% Rectum 48.0% 379 cg00666623 8 105375683 Colon 81.5% Rectum 61.3% 380 cg16345566 6 32633102 Colon 46.1% Rectum 66.3% 381 cg21780711 5 122765476 Cervix 49.2% Uterus 76.7% 382 cg26400750 14 22992073 NK-cells 60.7% CD8+ T-cells 18.4% 383 cg22986569 5 2659008 Colon 79.7% Rectum 59.6% 384 cg12145488 10 134514630 Colon 58.4% Rectum 78.5% 385 cg12673499 10 104412063 CD4+ T-cells 59.2% CD8+ T-cells 14.2% 386 cg20663852 20 50312386 Colon 47.4% Rectum 27.3% 387 cg13495373 7 28474813 Cervix 47.8% Uterus 75.3% 388 cg27491759 10 131988874 Esophagus 2.4% Stomach 42.8% 389 cg19373813 2 176989586 Colon 39.4% Rectum 59.5% 390 cg00664697 16 80061290 Bladder 14.7% Uterus 62.0% 391 cg08767838 20 62209642 Colon 54.6% Rectum 74.7% 392 cg25643644 11 118210017 NK-cells 52.7% CD8+ T-cells 10.4% 393 cg10590657 3 192538384 Colon 85.9% Rectum 65.9% 394 cg26508164 2 182650931 Cervix 40.4% Uterus 67.9% 395 cg15402992 20 44714513 Colon 52.6% Rectum 72.6% 396 cg02346442 3 186194046 Eosinophils 19.4% Neutrophils 75.7% 397 cg13939204 9 130667057 Colon 51.9% Rectum 31.9% 398 cg02202923 10 135040250 Breast 27.1% Uterus 78.2% 399 cg23497611 2 37985316 Cervix 37.1% Uterus 64.5% 400 cg10585941 1 214461479 CD4+ T-cells 60.4% CD8+ T-cells 15.9% 401 cg23255141 15 51199395 Colon 40.8% Rectum 60.8% 402 cg0OO12522 15 98503952 Esophagus 7.3% Stomach 47.4% 403 cg25283205 12 58333626 Colon 74.3% Rectum 54.3% 404 cg10509607 14 99658541 NK-cells 65.4% CD8+ T-cells 23.2% 405 cg13038847 22 28073997 Cervix 38.5% Uterus 65.9% 406 cg02645340 13 28553783 Colon 38.1% Rectum 58.1% 407 cg23426945 10 77190054 Cervix 61.3% Uterus 34.0% 408 cg12582008 13 53603286 Colon 60.3% Rectum 80.3% 409 cg03469682 3 100889156 Colon 51.9% Rectum 31.9% 410 cg05979457 8 105373628 Cervix 18.9% Uterus 46.2% 411 cg00705730 2 106438120 Monocytes 83.8% Neutrophils 11.6% 412 cg17818435 12 53497138 CD4+ T-cells 65.8% CD8+ T-cells 21.4% 413 cg08864645 6 33943929 Colon 56.7% Rectum 36.8% 414 cg09953583 14 22974144 NK-cells 76.6% CD8+ T-cells 34.6% 415 cg19759549 3 128211096 Colon 15.2% Rectum 35.1% 416 cg20282837 10 3508379 Eosinophils 24.5% Neutrophils 80.6% 417 cg24845296 3 142775293 Cervix 53.9% Uterus 81.2% 418 cg04060356 20 52226170 Colon 45.2% Rectum 25.3% 419 cg17997684 2 38151490 Colon 52.7% Rectum 72.6% 420 cg17632579 4 119274013 Esophagus 3.7% Stomach 43.8% 421 cg10901805 11 84634620 Colon 76.9% Rectum 57.0% 422 cg27006252 14 23391205 Cervix 47.6% Uterus 74.9% 423 cg05585149 10 695844 Esophagus 29.6% Head and neck 83.5% 424 cg07039378 7 73645723 NK-cells 28.9% CD8+ T-cells 70.8% 425 cg23256802 5 133313281 Colon 44.1% Rectum 63.9% 426 cg08506127 2 87018958 CD4+ T-cells 63.8% CD8+ T-cells 19.5% 427 cg27217253 17 8648562 Colon 45.9% Rectum 65.7% 428 cg19047868 17 46669485 Cervix 83.0% Uterus 55.8% 429 cg20521198 4 27102907 Colon 48.4% Rectum 28.6% 430 cg17346176 6 119239700 Colon 76.8% Rectum 57.0% 431 cg02046247 12 1662459 Cervix 42.7% Uterus 69.9% 432 cg26858152 8 140971523 Colon 70.9% Rectum 51.1% 433 cg13677149 7 27284789 Colon 38.6% Rectum 58.3% 434 cg23172995 5 150486801 Eosinophils 19.8% Neutrophils 75.8% 435 cg20852378 4 184680264 Colon 67.4% Rectum 47.7% 436 cg16582660 6 11329463 Cervix 42.4% Uterus 69.6% 437 cg20885910 11 48922741 Colon 86.2% Rectum 66.5% 438 cg02891314 5 179741120 Esophagus 53.8% Stomach 93.8% 439 cg23717186 1 226924311 NK-cells 64.9% CD8+ T-cells 23.1% 440 cg05059607 17 65488100 CD4+ T-cells 75.8% CD8+ T-cells 32.2% 441 cg17242937 4 111561114 Colon 53.5% Rectum 33.8% 442 cg15676528 2 9865398 Colon 58.2% Rectum 77.9% 443 cg06570967 10 96989650 Cervix 38.0% Uterus 65.2% 444 cg25595431 6 166261900 Colon 73.1% Rectum 92.7% 445 cg00225902 3 66444122 Bladder 62.1% Uterus 15.3% 446 cg05211868 4 184718046 Colon 31.6% Rectum 51.2% 447 cg01714284 4 185714524 Cervix 36.3% Uterus 63.5% 448 cg25373630 18 60984656 Colon 46.1% Rectum 65.7% 449 cg13163833 2 176954533 Colon 53.1% Rectum 72.7% 450 cg01476222 11 36522143 Monocytes 17.3% Neutrophils 89.4% 451 cg11067179 11 66083541 CD4+ T-cells 83.5% CD8+ T-cells 40.1% 452 cg08599635 16 78496508 Breast 83.5% Head and neck 35.7% 453 cg24410546 8 26371551 Esophagus 3.0% Stomach 42.8% 454 cg26239495 11 122791447 Colon 65.7% Rectum 85.3% 455 cg27656658 17 46628224 Cervix 78.9% Uterus 51.8% 456 cg04911180 3 176775716 Colon 57.3% Rectum 76.9% 457 cg20327324 4 24590014 Eosinophils 25.2% Neutrophils 80.9% 458 cg19854437 14 99891629 Colon 52.2% Rectum 71.7% 459 cg14689338 5 16742054 Cervix 34.7% Uterus 61.8% 460 cg07339236 20 50312490 Colon 48.2% Rectum 28.7% 461 cg15996644 12 54370352 Colon 44.9% Rectum 25.5% 462 cg26376241 2 65594021 Cervix 14.5% Uterus 41.6% 463 cg05408649 12 54446019 CD4+ T-cells 16.3% CD8+ T-cells 59.6% 464 cg13980266 9 97022269 Colon 60.4% Rectum 79.8% 465 cg20602300 15 40846077 Colon 74.4% Rectum 55.0% 466 cg13404054 19 15311666 Esophagus 14.4% Stomach 54.1% 467 cg10326726 10 51549505 Esophagus 34.8% Head and neck 88.6% 468 cg18498565 10 3173604 Colon 61.7% Rectum 81.1% 469 cg21185355 3 41975014 Cervix 24.3% Uterus 51.4% 470 cg08198540 14 22963867 NK-cells 72.5% CD8+ T-cells 30.9% 471 cg14526718 2 176932840 Colon 21.1% Rectum 40.4% 472 cg01016191 12 130707332 Cervix 82.0% Uterus 55.0% 473 cg04683418 17 21848728 Colon 89.8% Rectum 70.5% 474 cg26934362 5 1519265 CD4+ T-cells 78.0% CD8+ T-cells 34.7% 475 cg14384685 20 62209427 Colon 57.1% Rectum 76.4% 476 cg06775149 5 67818678 Cervix 45.5% Uterus 72.5% 477 cg05527785 17 46656543 Colon 39.8% Rectum 59.1% 478 cg18476766 2 240529742 Breast 79.6% Uterus 28.7% 479 cg13001142 6 147528521 Eosinophils 10.4% Neutrophils 66.1% 480 cg11196529 13 107182539 Colon 58.3% Rectum 77.5% 481 cg02979703 8 37558858 Colon 61.2% Rectum 80.4% 482 cg12101498 1 244829264 NK-cells 15.4% CD8+ T-cells 56.9% 483 cg15831212 5 139672796 Colon 62.7% Rectum 81.9% 484 cg08693997 15 95713013 Cervix 33.7% Uterus 60.7% 485 cg06400704 1 240256644 Colon 27.8% Rectum 8.6% 486 cg12422199 3 101284166 Monocytes 13.5% Neutrophils 85.5% 487 cg01756381 4 11430693 Esophagus 4.3% Stomach 43.8% 488 cg22801992 3 128210000 Colon 22.8% Rectum 42.0% 489 cg17636707 10 30970470 Cervix 46.4% Uterus 73.3% 490 cg10792302 12 54376019 Colon 71.5% Rectum 52.4% 491 cg03465513 13 28550046 Colon 23.2% Rectum 42.3% 492 cg14089436 9 20624421 Colon 44.5% Rectum 63.6% 493 cg08033722 12 54370458 Colon 40.8% Rectum 21.7% 494 cg01961086 1 3086487 Esophagus 43.6% Head and neck 96.5% 495 cg10032599 16 51560612 Cervix 27.3% Uterus 54.2% 496 cg07917644 15 101766122 Eosinophils 16.8% Neutrophils 72.4% 497 cg07679948 12 56329641 NK-cells 88.4% CD8+ T-cells 47.0% 498 cg26783428 1 41156791 Colon 32.0% Rectum 51.1% 499 cg05120668 1 78275707 Cervix 42.9% Uterus 69.8% 500 cg20325547 1 9431594 Colon 60.6% Rectum 41.5%

For deconvolution, it was assumed that a cfDNA methylation profile is a linear combination of the methylation profiles of the cell types which contribute to cfDNA. Under this assumption, the relative contributions of different cell types can be determined using non-negative least squares regression (NNLS) (see illustration of the process in FIG. 2C).

in silico experiments were performed initially to assess performance of the deconvolution approach in determining the relative contributions of various cell types to a methylation profile of DNA from a heterogeneous mixture of cell types. DNA methylation profiles of individual samples of cell types and tissues were computationally mixed with DNA methylation profiles of leukocytes (FIG. 3A) and the performance of the deconvolution algorithm on these mixes was assessed (when using a reference atlas not including the specific methylomes mixed in silico). It was found that every cell type in the analysis (whose methylome is present in the atlas) could be detected when composing >1% of the mixture, with many cell types detected even when present at <1% in the mixture. Importantly, very little presence of non-leukocyte cells was detected when leukocytes alone were analyzed (FIG. 3A). When an atlas lacking the feature-selection described herein was used, detection was far less accurate and required the cell or tissue to comprise a greater percent of the cfDNA total (FIG. 3B).

As expected, purified cell types mixed into blood methylomes were more easily detected than whole tissues, which represents heterogeneous mixtures of different cell types (FIG. 3C-D). An atlas containing the methylomes of purified pancreatic cells was superior in detecting pancreatic DNA within blood compared to a reference atlas containing the methylome of the whole pancreas (FIG. 3C). Similarly, use of a hepatocyte methylome in place of whole liver or HepG2 cells was superior in detecting cfDNA (FIG. 3D). Importantly, the advantage of using a cell type-specific reference rather than a tissue-specific reference was most evident when the tissue of interest was contributing <2% to the mixed methylome. Lastly, a pancreas only atlas, comprising healthy cfDNA methylomes and methylomes from three pancreatic cell types (acinar, beta and duct) was capable of identifying each cfDNA of each of those 3 cell types (FIG. 3E). These findings support the feasibility of highly sensitive deconvolution of the plasma methylome, and highlight the importance of using a comprehensive, cell type specific, methylome atlas for sensitive detection of rare contributors to mixed methylomes.

Example 3: Healthy cfDNA Contributors

To determine the main contributors to cfDNA in healthy individuals, plasma was collected from multiple healthy donors, and the donors were classified as male or female, young (age 19-30) or elder (age 75+), and cfDNA present in plasma according to these groups was pooled, to reach 250 ng cfDNA in each pool. Additionally, in a few cases blood draws of ˜100 ml from a single donor were possible, so as to avoid pooling. Methylation profiles of the samples were then obtained using the Illumina EPIC arrays and deconvolution analysis was performed. The predicted distribution of tissue sources was very similar among the different pools and between individual healthy donors (FIG. 4A-B). As has been previously reported, it was found that the main contributors to cfDNA were of hematopoietic origin. On average, 30.5% of cfDNA came from granulocytes, 31.8% from erythrocyte progenitors (likely representing the process by which erythrocyte progenitor cell lose their DNA during differentiation in the bone marrow), 18.3% from monocytes, and 7.3% from lymphocytes. The main solid tissue contributions to cfDNA were from vascular endothelial cells (˜10%) and hepatocytes (˜1.3%). The signal from erythrocyte progenitors, endothelial cells and hepatocytes is expected to be present in cfDNA but not in DNA isolated from leukocytes. Indeed, deconvolution of blood methylomes predicted signals from these tissues at much lower levels than in plasma, supporting validity of the algorithm (FIG. 4C). While the young and elder samples showed similar relative contributions of the different cell types, the plasma of older people showed higher absolute cfDNA levels for the all cell types (genome equivalents/ml). While this could indicate increased cell death in older individuals, the finding that cfDNA from all tissues was essentially affected equally suggests a slower clearance rate of circulating DNA in elder individuals (FIG. 4B). These findings provide a first detailed description of the composition of cfDNA in healthy subjects.

Example 4: Deconvolution of cfDNA in Pancreatic Islet Transplant Recipients

As the analysis of cfDNA is emerging as an important tool in the monitoring of organ transplant recipients, the potential for a cfDNA methylome deconvolution approach to identify DNA from a transplanted organ in circulation was evaluated. The methylome profile generated from pooled cfDNA of type 1 diabetes patients 1 hour after receiving a cadaveric islet transplant was determined. The total concentration of cfDNA in these samples was ˜20× higher than healthy control levels, suggesting a massive process of cell death shortly after islet transplantation. The deconvolution algorithm identified that a large proportion of the DNA (˜17%) was of pancreatic origin, both beta cells and acinar cells (FIG. 5A), which had never been observed in healthy plasma. Each cell type was significantly increased on its own and also all pancreatic cells were increases when the two were taken together. These findings provide further support to the validity of the deconvolution procedure. More strikingly, it was found that most of the increase in cfDNA levels in islet transplant recipients was of an immune cell origin (granulocytes, monocytes and lymphocytes, but not erythrocyte progenitors), suggesting an acute immune response to transplantation resulting in massive immune cell death (FIG. 5B).

Additional control experiments were performed to assess assay validity. First, the predictions of the deconvolution algorithm for pancreatic cfDNA in the plasma of patients was assessed before (<1 day), 1 hour after, and 2 hours after transplantation. Negligible levels of pancreas cfDNA were found before islet transplantation, and a large increase immediately after transplantation, and a subsequent decrease in levels of pancreatic cfDNA, was also observed as expected (FIG. 5C). Second, the levels of beta cell cfDNA predicted by the deconvolution algorithm were correlated to the levels measured by specific analysis of the unmethylated insulin gene as has been reported in the art. The prediction of deconvolution agreed well with the method based on PCR and sequencing (FIG. 5D).

Finally, the performance of the deconvolution algorithm was tested when using a reference matrix containing either a whole pancreas methylome or pancreatic cell specific methylomes (acinar, duct and beta cells). Consistent with results from deconvolution of in silico mixes (FIG. 3A-D), a reference matrix containing cell type-specific methylomes proved highly sensitive in the detection of rare pancreas cell contribution to plasma in the transplant setting; more sensitive than when the reference matrix contained a whole pancreas methylome (FIG. 5E). Of great importance, two of the patients tested only showed an increase in pancreatic cfDNA with the cell type-specific methylome atlas (blue and red solid lines), whereas when analysis was performed with a reference matrix from whole pancreases no change in pancreatic cfDNA was observed at any time point (blue and red dashed lines).

Example 5: Cellular Contributors to cfDNA in Sepsis

An increase in total cfDNA levels in septic patients has been previously documented, and even shown to have a prognostic value. However, it is unclear which cell types are contributing to elevated cfDNA. Therefore, the cfDNA methylation profile of 15 samples from patients with sepsis were analyzed. The main contributors to the increase in cfDNA in these patients relative to healthy levels were leukocytes (mainly neutrophils) in most cases (12/15) (FIG. 6A-B). In one case, a large amount of DNA of gastrointestinal origin was detected, consistent with the fact the donor had developed sepsis after an intestinal perforation during surgery to remove a tumor of intestinal origin (FIG. 6C). Hepatocyte cfDNA was also detected likely owing to remaining metastases in the liver. In other cases, varying amounts of hepatocyte cfDNA were detected (FIGS. 6A, and 6D). Importantly, the levels of hepatocyte cfDNA were strongly correlated with levels of Alanine Transferase (ALT) in circulation, a marker of hepatocyte damage (FIG. 6E).

Example 6: Identifying Tumor Origins by cfDNA Methylation

cfDNA methylation profiles of three patients with metastatic colon cancer were analyzed, all of whom presented with elevated overall levels of cfDNA compared to healthy individuals. In these cases, most of the increase in cfDNA could be defined as gastrointestinal in origin (FIG. 7A). Next an attempt was made to determine whether an unbiased deconvolution approach could be useful in identifying a cancer tissue of origin in patients with metastatic disease, even in the absence of an identifiable primary tumor. To this end, the cfDNA of three patients with Cancer of Unknown Primary (CUP) were analyzed. All patients had metastatic disease with no clear pathological identification of the primary source of cancer. In each case the suspected origin of the tumor, based on clinical history of the patient (e.g. a local bladder carcinoma that was previously treated is suspected as the source of new bone metastases) showed a strong signal in the deconvolution analysis (FIG. 7B). Importantly, in all 3 cases the clinically suspected tissue gave a signal only in the cfDNA of the relevant patient. All CUP patients also had a strong stomach signal in cfDNA; it is not clear if this reflects an error of the deconvolution algorithm or true (possibly treatment-associated) intestinal damage. These findings indicate that cfDNA methylation deconvolution can be the basis of a new non-invasive approach to identify the origin of cancer.

Example 7: Reproducibility

To assess the reproducibility of the deconvolution analysis, three cfDNA samples were assayed in duplicate (FIG. 8A). The predicted proportions of cell types contributing to the samples were very highly correlated (r>0.995) (FIG. 8B). Furthermore, as limited amounts of cfDNA are often available, the possibility of using less than the 250 ng DNA recommended by Illumina for analysis with the Illumina methylation array was evaluated. The results were reproducible with as little as 50 ng of cfDNA (average r=0.964) (FIG. 8B).

Although the invention has been described in conjunction with specific embodiments thereof, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, it is intended to embrace all such alternatives, modifications and variations that fall within the spirit and broad scope of the appended claims. 

1. A method of determining the cell type or tissue of origin of cell free DNA (cfDNA) comprising: a. providing cfDNA; b. measuring DNA methylation of said cfDNA; and c. assigning a cfDNA molecule from said cfDNA to a cell type or tissue of origin by comparing the methylation of said molecule to a methylome atlas of at least 1 cell type or tissue, wherein said atlas comprises at least 25 of the 100 most uniquely methylated sites and at least 25 of the 100 most uniquely unmethylated sites in each of said at least 1 cell type or tissue; thereby determining the cell or tissue of origin of cfDNA, optionally wherein cfDNA of said tissue or cell type comprises as little 1% of all of said cfDNA.
 2. The method of claim 1, for detecting death of a cell type or tissue in a subject, wherein said cfDNA is from said subject, and wherein determining the origin cell type or tissue of said cfDNA indicates death of said origin cell type or tissue in said subject; optionally for use in detecting a disease state in a subject in need the thereof, wherein death of said cell type or tissue is indicative of said disease state, optionally wherein said disease state is selected from organ transplantation, sepsis, and cancer; optionally wherein said method determined the cell or tissue of origin of said cancer.
 3. (canceled)
 4. (canceled)
 5. The method of claim 1, wherein said providing comprises providing a bodily fluid and isolating said cfDNA from said bodily fluid or providing at least 50 ng of cfDNA.
 6. The method of claim 1, wherein said measuring DNA methylation comprises bisulfite conversion of said cfDNA; optionally wherein said measuring further comprises performing a methylome array or chip on said bisulfite converted cfDNA.
 7. The method of claim 1, wherein said methylome atlas comprises a. only data from purified cell types; optionally wherein said purified cell types are only non-blood derived purified cell types; b. methylation data from at least 5 of the following 34 tissues or cell types: monocytes, B-cells, CD4+ T-cells, NK-cells, CD8+ T-cells, eosinophils, neutrophils, erythrocyte progenitors, adipocytes, neurons, hepatocytes, lung alveolar cells, pancreatic beta cells, pancreatic acinar cells, pancreatic duct cells, vascular endothelial cells, left atrium, bladder, breast, cervix, colon, esophagus, oral cavity, kidney, prostate, rectum, stomach, thyroid, uterus, lung bronchial cells, cholangiocytes, muscle, oligodendrocytes, and ovary; optionally wherein said methylome atlas comprises data from all of said 34 tissues or cell types; c. at least the 100 most uniquely methylated or unmethylated sites in each tissue or cell type.
 8. (canceled)
 9. (canceled)
 10. The method of claim 1, wherein said methylome atlas further comprises any CpG sites within at least 150 base pairs upstream and downstream of said most uniquely methylated and most uniquely unmethylated sites in each tissue or cell type; optionally wherein said CpG sites within at least 150 base pairs upstream and downstream are selected from Tables 1 and
 2. 11. The method of claim 1, wherein said methylome atlas further comprises at least one of the 500 CpG sites that best differentiate between the most similar pairs of tissues and cell types; optionally wherein said 500 CpG sites that best differentiate between the most similar pairs of tissues and cell types are selected from Table
 3. 12. The method of claim 1, wherein said most uniquely methylated sites are selected from Table 1 and wherein said most uniquely hypomethylated sites are selected from Table
 2. 13. (canceled)
 14. A method of constructing a methylome atlas, comprising: a. providing DNA methylation data from at least 5 tissues and/or cell types; b. selecting at least the top 100 uniquely hyper-methylated and at least the top 100 uniquely hypomethylated CpG sites per a tissue and/or cell type as compared to the other tissues and/or cell types; and c. combining said uniquely hyper-methylated and uniquely hypo-methylated sites for all said tissues and/or cell types; thereby constructing a methylome atlas.
 15. The method of claim 14, further comprising: a. selecting all neighboring CpG sites within at least 150 base pairs upstream and/or downstream of each of said uniquely hyper-methylated and uniquely hypomethylated CpG site; and b. combining said neighboring CpG sites with said uniquely hyper-methylated and uniquely hypomethylated CpG sites, and optionally further comprising c. selecting at least the top 100 CpG sites that best differentiate between the most similar pair of tissues and/or cell types; and d. combining said CpG sites that best differentiate with said uniquely hyper-methylated and uniquely hypomethylated CpG sites.
 16. (canceled)
 17. A computerized method of determining the cell type or tissue of origin of cell free DNA (cfDNA) comprising: using at least one hardware processor to: a. receive a methylation sequencing data acquired from a cfDNA sample and a methylome atlas, wherein the methylome atlas comprises (i) a first set of uniquely methylated sites, (ii) a plurality of neighboring methylated sites, wherein at least some of the plurality of neighboring methylated sites are within 500 base units to each element of the first set of uniquely methylated sites, and (iii) a second set of uniquely methylated sites comprising more than double the number of sites of the first set, and comprising less than half the number of tissue types in each comparison as the first set; b. compare the methylation sequencing data with a methylome atlas to determine a set of candidate tissues, wherein the comparing comprises identifying at least some elements of the first set of uniquely methylated sites in both the methylation sequencing data and the methylome atlas, optionally wherein the comparing comprises using a latent probabilistic model; c. compare the plurality of neighboring methylated sites of the methylation sequencing data with the atlas entries of the set of candidate tissues to determine a first subset of candidate tissues, optionally wherein the comparing comprises using a latent probabilistic model; d. compare the second set of uniquely methylated sites of the methylome atlas within the methylation sequencing data to determine a probability ranking of candidate tissues, optionally wherein the comparing comprises using a latent probabilistic model; and e. assign at least one of the highest-ranking tissues of the probability ranking of candidate tissues to the cfDNA sample.
 18. The method of claim 17, wherein the first set comprises between 25 and 100 most uniquely methylated sites or a plurality of most uniquely methylated sites and wherein the plurality of neighboring methylated sites comprises any CpG sites within between 150 and 500 base pairs upstream and downstream of said most uniquely methylated sites in each tissue or cell type.
 19. (canceled)
 20. The method of claim 17, wherein the second set comprises between 100 and 500 most uniquely methylated sites or compares a plurality of specific pairs or triplets of similar tissue types.
 21. (canceled)
 22. The method of claim 17, wherein the first set of uniquely methylated sites are uniquely methylated as compared to all cell types and tissues of the atlas, and wherein said second set of uniquely methylated sites are uniquely methylated in one cell type or tissue as compared to a second most similar cell type or tissue.
 23. (canceled)
 24. A computer program product for determining the cell or tissue of origin of cell free DNA (cfDNA), comprising a non-transitory computer-readable storage medium having program code embodied thereon, the program code executable by at least one hardware processor to a. i) measure or access measurements of DNA methylation of cfDNA; ii) assign a cfDNA molecule from said cfDNA to a cell type or tissue of origin by comparing the methylation of said molecule to a methylome atlas of at least 5 cell types or tissues, wherein said atlas comprises at least 25 of the 100 most uniquely methylated sites and at least 25 of the 100 most uniquely unmethylated sites in each of said 5 cell types or tissues; and iii) provide an output regarding the cell or tissue of origin of cfDNA; or b. i) receive a methylation sequencing data acquired from a cfDNA sample and a methylome atlas, wherein the methylome atlas comprises (i) a first set of uniquely methylated sites, (ii) a plurality of neighboring methylated sites, wherein at least some of the plurality of neighboring methylated sites are within 500 base units to each element of the first set of uniquely methylated sites, and (iii) a second set of uniquely methylated sites comprising more than double the number of sites of the first set, and comprising less than half the number of tissue types in each comparison as the first set; ii) compare the methylation sequencing data with a methylome atlas to determine a set of candidate tissues, wherein the comparing comprises identifying at least some elements of the first set of uniquely methylated sites in both the methylation sequencing data and the methylome atlas, optionally wherein the comparing comprises using a latent probabilistic model; iii) compare the plurality of neighboring methylated sites of the methylation sequencing data with the atlas entries of the set of candidate tissues to determine a first subset of candidate tissues, optionally wherein the comparing comprises using a latent probabilistic model; iv) compare the second set of uniquely methylated sites of the methylome atlas within the methylation sequencing data to determine a probability ranking of candidate tissues, optionally wherein the comparing comprises using a latent probabilistic model; and v) assign at least one of the highest-ranking tissues of the probability ranking of candidate tissues to the cfDNA sample.
 25. (canceled)
 26. The computer program product of claim 24, wherein the first set comprises between 25 and 100 most uniquely methylated sites or a plurality of most uniquely methylated sites and wherein the plurality of neighboring methylated sites comprises any CpG sites within between 150 and 500 base pairs upstream and downstream of said most uniquely methylated sites in each tissue or cell type.
 27. (canceled)
 28. The computer program product of claim 24, wherein the second set comprises between 100 and 500 most uniquely methylated sites or compares a plurality of specific pairs or triplets of similar tissue types.
 29. (canceled)
 30. The computer program product of claim 24, wherein the first set of uniquely methylated sites are uniquely methylated as compared to all cell types and tissues of the atlas, and wherein said second set of uniquely methylated sites are uniquely methylated in one cell type or tissue as compared to a second most similar cell type or tissue.
 31. (canceled)
 32. A computerized system for determining the cell or tissue of origin of cell free DNA (cfDNA), comprising: at least one hardware processor; and the computer program product of claim
 24. 33. The computerized system product of claim 32, wherein a. the first set comprises between 25 and 100 most uniquely methylated sites; b. the first set comprises a plurality of most uniquely methylated sites and wherein the plurality of neighboring methylated sites comprises any CpG sites within between 150 and 500 base pairs upstream and downstream of said most uniquely methylated sites in each tissue or cell type; c. the second set comprises between 100 and 500 most uniquely methylated sites; d. the second set of uniquely methylated sites compares a plurality of specific pairs of tissue types; or e. the first set of uniquely methylated sites are uniquely methylated as compared to all cell types and tissues of the atlas, and wherein said second set of uniquely methylated sites are uniquely methylated in one cell type or tissue as compared to a second most similar cell type or tissue. 34-37. (canceled) 